Instructions

Instructions for downloading a database

 Except check database (see at the end)

 

Download the license agreement (next to the database description), fill it out, sign it by a faculty and send it by email to gpds@gi.ulpgc.es, as follows:

Subject: TOOLBOX/DATABASE download

Body: Your name, e-mail, phone number, organization, postal mail, Database you require, purpose for which you will use the database, time and date at which you sent the fax with the signed license agreement. Along with the email send a pdf file with the signed license agreement.


Once the email of the license agreement have been received, you will receive an email with instructions to download the database. After you finish the download, please notify by email that you have successfully completed the transaction.

For more information, please contact: gpds@gi.ulpgc.es

 

TOOLBOX
 

Novel Anthropomorphic Features For On-line Signatures

 

 

A novel feature space for on-line signatures is available for researching purposes. They characterize the movement of the shoulder, the elbow and the wrist joints when signing through a virtual skeletal arm (VSA) model, which simulates the architecture of a real arm and forearm. These features are named "Anthropomorphic Features" which are divided into Position-based anthropomorphic features and Angle-based anthropomorphic features.

 

The producedure of obtaining these features are detailed in the articles to cite.

 

To obtain the anthropomorphic features, you can write the following instructions in Matlab:

 

[Q, pe, pw, pf] = pos2angL2L4(x,y,z,az,in,L2, L4);

 

The inputs x,y,z correspond to spatial trajectory of the signature in mm. L2,L4 denote the humerus and radius/ulna lengths. The output Q is a vector with the six angle-based features. The parameters pe, pw, pf denote the 3D position of the elbow, wrist and fingers, respectively.

 

The function pos2angL2L4 is developed in Matlab and its source code can be freely downloaded for researching purposes. Once you send us to gpds@gi.ulpgc.es a signed copy of the following licence agreement, we send you by email the source code to get the anthropomorphic features.

 

For any remarks about this code, do not hesitate to contact the authors sending an e-mail to: gpds@gi.ulpgc.es

 

LICENSE AGREEMENT FOR NON-COMMERCIAL RESEARCH USE OF anthropomorphic features for on-line signatures 

Article to cite: Diaz, M., Ferrer, M. A., & Quintana, J. J. (2018). Anthropomorphic features for on-line signatures. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(12), 2807-2819. doi: 10.1109/TPAMI.2018.2869163

@article{diaz2018anthropomorphic, title={Anthropomorphic features for on-line signatures}, author={Diaz, Moises and Ferrer, Miguel A and Quintana, Jose J}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume={41}, number={12}, pages={2807--2819}, year={2018}, publisher={IEEE} }

Article to cite: M. Diaz, M.A. Ferrer and J.J. Quintana. Robotic Arm Motion for Verifying Signatures. 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), Niagara Falls, USA, 5-8 August 2018, pp. 157-162. doi: 10.1109/ICFHR-2018.2018.00036

@inproceedings{diaz2018robotic, title={Robotic arm motion for verifying signatures}, author={Diaz, Moises and Ferrer, Miguel A and Quintana, Jose J}, booktitle={2018 16th International conference on frontiers in handwriting recognition (ICFHR)}, pages={157--162}, year={2018}, organization={IEEE} }

 

 

gpds Synthetic Duplicator Engine for Static Signatures

 

 

A synthetic duplicator engine for static signatures is available. It is developed in Matlab and it is distributed in *.p format.

The duplicator is based on a set of nonlinear and linear transformations which simulate the human spatial cognitive map and motor system intra-personal variability during the signing process.

 

The procedure of this duplicator is detailed in:

 

M. Diaz, M. A. Ferrer, G. Eskander, R. Sabourin, (2016), "Generation of Duplicated Off-line Signature Images for Verification Systems", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 5, pp. 951-964. doi: 10.1109/TPAMI.2016.2560810

 

M. Diaz, M. A. Ferrer and R. Sabourin (2016). "Approaching the Intra-Class Variability in Multi-Script Static Signature Evaluation". 23rd International Conference on Pattern Recognition (ICPR), Cancun, Mexico. doi: 10.1109/ICPR.2016.7899791.

 

 

To use the duplicator, you should write the following code in Matlab

 

Iin=im2double(imread('my_signature.png'));

out = off2off(Iin);

subplot(121);imagesc(Iin);colormap(gray);subplot(122);imagesc(out);colormap(gray);

 

 

For any remarks about this toolbox, do not hesitate to contact the authors sending an e-mail to: gpds@gi.ulpgc.es

 

LICENSE AGREEMENT FOR NON-COMMERCIAL RESEARCH USE OF SyntheticDuplicatorEngineForStaticSignatures 

Article to cite: Diaz-Cabrera, M., Ferrer, M. A., & Morales, A. (2014, September). Cognitive inspired model to generate duplicated static signature images. In 2014 14th International Conference on Frontiers in Handwriting Recognition (pp. 61-66). IEEE. doi: 10.1109/ICFHR.2014.18.

@inproceedings{diaz2014cognitive, title={Cognitive inspired model to generate duplicated static signature images}, author={Diaz-Cabrera, Moises and Ferrer, Miguel A and Morales, Aythami}, booktitle={2014 14th International Conference on Frontiers in Handwriting Recognition}, pages={61--66}, year={2014}, organization={IEEE} }

Article to cite: Diaz-Cabrera, M., Ferrer, M. A., & Morales, A. (2015). Modeling the lexical morphology of western handwritten signatures. PloS one, 10(4), e0123254. doi: 10.1371/journal.pone.0123254

@article{diaz2015modeling, title={Modeling the lexical morphology of western handwritten signatures}, author={Diaz-Cabrera, Moises and Ferrer, Miguel A and Morales, Aythami}, journal={PloS one}, volume={10}, number={4}, pages={e0123254}, year={2015}, publisher={Public Library of Science San Francisco, CA USA} }

Article to cite: Diaz, M., Ferrer, M. A., Eskander, G. S., & Sabourin, R. (2016). Generation of duplicated off-line signature images for verification systems. IEEE transactions on pattern analysis and machine intelligence, 39(5), 951-964. doi: 10.1109/TPAMI.2016.2560810

@article{diaz2016generation, title={Generation of duplicated off-line signature images for verification systems}, author={Diaz, Moises and Ferrer, Miguel A and Eskander, George S and Sabourin, Robert}, journal={IEEE transactions on pattern analysis and machine intelligence}, volume={39}, number={5}, pages={951--964}, year={2016}, publisher={IEEE} }

Article to cite: Diaz, M., Ferrer, M. A., & Sabourin, R. (2016, December). Approaching the intra-class variability in multi-script static signature evaluation. In 2016 23rd international conference on pattern recognition (ICPR) (pp. 1147-1152). IEEE. doi: 10.1109/ICPR.2016.7899791.

@inproceedings{diaz2016approaching, title={Approaching the intra-class variability in multi-script static signature evaluation}, author={Diaz, Moises and Ferrer, Miguel A and Sabourin, Robert}, booktitle={2016 23rd international conference on pattern recognition (ICPR)}, pages={1147--1152}, year={2016}, organization={IEEE} }

 

 

gpds HMM

 

A Hidden Markov Model (HMM) Toolbox within the Matlab environment is available. In this toolbox, the conventional techniques for the continuous and discrete HMM are developed for the training as well as for the test phases. The ability to make different groups of components for the vector pattern is provided. Multi-labeling techniques for the discrete HMM is also provided. The toolbox includes procedures suitable for the classical applications based on the HMM, as pattern recognition, speech recognition and DNA sequence analysis.

 

This toolbox is distributed as binary (dll files) and source code format. For a wide promotion, we ask the users to make a reference to the paper:

Article to cite: David, S., Ferrer, M. A., Travieso, C. M., & Alonso Hernández, J. B. (2004). gpdsHMM: a hidden Markov model toolbox in the Matlab Enviroment. Int. Conf on Complex System Intelligence and Modern Technology Application, Cherbourg, Normandy, France (p. 19-22).

 

@article{david2004gpdshmm, title={gpdsHMM: a hidden Markov model toolbox in the Matlab Enviromen}, author={David, S{\'e}bastien and Ferrer, Miguel A and Travieso, Carlos Manuel and Alonso Hern{\'a}ndez, Jes{\'u}s Bernardino}, year={2004} }

For any remarks about this toolbox, do not hesitate to contact the authors sending an e-mail to: gpds@gi.ulpgc.es

 

 

 

Paper Referent    

 

user's guide    

download  gpdsHMM

DATABASE

 

 

 

 

CowScreeningDB: A public benchmark database for lameness detection in dairy cows

CowScreeningDB: A public benchmark database for lameness detection in dairy cows

 

We are thrilled to announce the launch of CowScreeningDB, a multi-sensor database constructed from data collected from 43 dairy cows. These cows were meticulously monitored using state-of-the-art smart watches during their regular daily activities.

 

CowScreeningDB is a treasure trove of information that offers an objectively comparable resource for the further development of techniques for lameness detection in dairy cows. This makes it an invaluable tool for researchers and developers alike.

 

Along with providing public access to the database, we also present a machine learning technique that classifies cows as healthy or lame using raw sensory data. To ensure fair comparisons with state-of-the-art methods, we've introduced a novel benchmark.

LICENSE AGREEMENT FOR NON-COMMERCIAL RESEARCH USE OF THESE DATABASES

Thank you for using our database in your research! We kindly request that you acknowledge our contribution by citing our work in your publications.

 

Proper citation ensures that our team and collaborators receive recognition for the effort and resources that went into creating the database. It also helps other researchers to find and use our work, enabling the scientific community to benefit from the database.

 

We appreciate your collaboration and look forward to supporting your research endeavors.

Article to cite: Shahid Ismail, Moises Diaz, Cristina Carmona-Duarte, Jose Manuel Vilar and Miguel A. Ferrer (2023). "CowScreeningDB: A public benchmark dataset for lameness detection in dairy cows". Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2023.108500

@article{ismail2024cowscreeningdb, title={CowScreeningDB: A public benchmark database for lameness detection in dairy cows}, author={Ismail, Shahid and Diaz, Moises and Carmona-Duarte, Cristina and Vilar, Jose Manuel and Ferrer, Miguel A}, journal={Computers and Electronics in Agriculture}, volume={216}, pages={108500}, year={2024}, publisher={Elsevier} }

 

 

 

 

 

Synthesis of 3D On-Air Signatures and gestures

Synthesis of 3D On-Air Signatures and gestures

 

We are proud to offer you a range of high-quality synthetic 3D signature and gesture databases that closely mimic the real ones. Our signature databases, Signature3DIIT and Deep3DSigAir, are expertly designed to replicate the intricate details of real signatures. Our synthetic 3D gesture databases, which include AirWriting, HDM05 and UTKinect, offer a close approximation of human gestures.

 

In addition, we provide specimens with three different synthesis methods: full-based synthesis (FS), kinematic synthesis (KS), and duplicated specimens used in our experiments. This allows you to choose the specimen that best suits your needs and research goals.

 

At our website, you'll find a comprehensive set of tools and resources to help you explore our synthetic 3D signature and gesture databases. We are committed to providing you with the highest quality data to support your research and help you achieve your goals.

LICENSE AGREEMENT FOR NON-COMMERCIAL RESEARCH USE OF THESE DATABASES

Thank you for using our database in your research! We kindly request that you acknowledge our contribution by citing our work in your publications.

 

Proper citation ensures that our team and collaborators receive recognition for the effort and resources that went into creating the database. It also helps other researchers to find and use our work, enabling the scientific community to benefit from the database.

 

We appreciate your collaboration and look forward to supporting your research endeavors.

Article to cite: M. A. Ferrer, M. Diaz, Cristina Carmona-Duarte, Jose Juan Quintana, Rejean Plamondon (2023). "Synthesis of 3D On-Air Signatures with the Sigma-Lognormal Model", Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2023.110365

@article{ferrer2023synthesis, title={Synthesis of 3D on-air signatures with the Sigma--Lognormal model}, author={Ferrer, Miguel A and Diaz, Moises and Carmona-Duarte, Cristina and Quintana, Jose Juan and Plamondon, R{\'e}jean}, journal={Knowledge-Based Systems}, volume={265}, pages={110365}, year={2023}, publisher={Elsevier} }

Article to cite: M. Diaz, M. A. Ferrer, C. Carmona-Duarte, J. J. Quintana, R. Plamondon, A. Morales, J. Fierrez (2022), "Kinematic Synthesis for 3D Signatures," 2022 IEEE International Joint Conference on Biometrics (IJCB), Abu Dhabi, United Arab Emirates, 2022, pp. 1-7, doi: 10.1109/IJCB54206.2022.10007945. .

@inproceedings{diaz2022kinematic, title={Kinematic Synthesis for 3D Signatures}, author={Diaz, Moises and Ferrer, Miguel A and Carmona-Duarte, Cristina and Quintana, Jose Juan and Morales, Aythami and Fierrez, Julian and Plamondon, R{\'e}jean}, booktitle={2022 IEEE International Joint Conference on Biometrics (IJCB)}, pages={1--7}, year={2022}, organization={IEEE} }

 

 

 

 

 

 

 

Kinematic Synthesis for 3D Signatures

Kinematic Synthesis for 3D Signatures

 

We designed the velocity of 3D signatures through iDeLog3D, which implements the kinematic theory of rapid movements for 3D spatiotemporal signals. Here we provide two synthetic signatures databases with this method. First, we removed the velocity of the signatures by 8-connecting the sampling points in the trajectory. Then, using this input, the method estimated the position and number of the strokes in the trajectory to generate the velocity profile. In this work, we used the 3DIIT Signatures database and Deep3DSigAir database.

LICENSE AGREEMENT FOR NON-COMMERCIAL RESEARCH USE OF THESE DATABASES

Please, cite our work if you find useful the database in your research:

Article to cite: M. Diaz, M. A. Ferrer, C. Carmona-Duarte, J. J. Quintana, R. Plamondon, A. Morales, J. Fierrez (2022), Kinematic Synthesis for 3D Signatures," 2022 IEEE International Joint Conference on Biometrics (IJCB), Abu Dhabi, United Arab Emirates, 2022, pp. 1-7, doi: 10.1109/IJCB54206.2022.10007945. .

@inproceedings{diaz2022kinematic, title={Kinematic Synthesis for 3D Signatures}, author={Diaz, Moises and Ferrer, Miguel A and Carmona-Duarte, Cristina and Quintana, Jose Juan and Morales, Aythami and Fierrez, Julian and Plamondon, R{\'e}jean}, booktitle={2022 IEEE International Joint Conference on Biometrics (IJCB)}, pages={1--7}, year={2022}, organization={IEEE} }

 

 

 

 

 

 

 

MDIW-13: A new Multi-lingual and Multi-script Database and Benchmark for Script Identification

MDIW-13: A new Multi-lingual and Multi-script Database and Benchmark for Script Identification

 

Script identification is a necessary step in some applications involving document analysis in a multi-script and multi-language environment. This paper provides a new database for benchmarking script identification algorithms, which contains both printed and handwritten documents collected from a wide variety of scripts, such as Arabic, Bengali (Bangla), Gujarati, Gurmukhi, Devanagari, Japanese, Kannada, Malayalam, Oriya, Roman, Tamil, Telugu, and Thai. The dataset consists of 1,135 documents scanned from local newspapers and handwritten letters and notes from different native writers. Further, these documents are segmented into lines and words, comprising a total of 13,979 and 86,655 lines and words, respectively, in the dataset. Easy-to-go benchmarks are proposed with handcrafted and deep learning methods. The benchmark includes results at the document, line, and word levels with printed and handwritten documents. Results of script identification independent of the document/line/word level and independent of the printed/handwritten letters are also given:

The database can be freely downloaded for research purposes at:

DOWNLOAD THE DATABASE NOW!

Please, cite our works if you find useful the database:

Ferrer, M. A., Das, A., Diaz, M., Morales, A., Carmona-Duarte, C., & Pal, U. (2024). MDIW-13: a New Multi-Lingual and Multi-Script Database and Benchmark for Script Identification. Cognitive Computation, 16(1), 131-157. https://doi.org/10.1007/s12559-023-10193-w

@article{ferrer2024mdiw, title={MDIW-13: a New Multi-Lingual and Multi-Script Database and Benchmark for Script Identification}, author={Ferrer, Miguel A and Das, Abhijit and Diaz, Moises and Morales, Aythami and Carmona-Duarte, Cristina and Pal, Umapada}, journal={Cognitive Computation}, volume={16}, number={1}, pages={131--157}, year={2024}, publisher={Springer} }

Das, A., Ferrer, M. A., Morales, A., Diaz, M., Pal, U., Impedovo, D., ... & Gattal, A. (2021). ICDAR 2021 Competition on Script Identification in the Wild. In Document Analysis and Recognition–ICDAR 2021: 16th International Conference, Lausanne, Switzerland, September 5–10, 2021, Proceedings, Part IV 16 (pp. 738-753). Springer International Publishing. doi: 10.1007/978-3-030-86337-1_49

@article{das2021siw, title={SIW 2021: ICDAR Competition on Script Identification in the Wild}, author={Das, Abhijit and Ferrer, Miguel A and Morales Moreno, Aythami and D{\'\i}az Cabrera, Mois{\'e}s and Pal, Umapada and Impedovo, Donato and Li, Hongliang and Yang, Wentao and Ota, Kensho and Yao, Tadahito and others}, year={2021}, publisher={Springer} }

 

 

 

 

 

 

 

GPDS Bengali and Devanagari Synthetic Signature Databases

OffOnSyntheticBengaliSignatures and OffOnSyntheticDevanagariSignatures databases

 

Dual Off line and On line signature databases of Bengali and Devanagari signatures. It contains data from 100 synthetic individuals: 24 genuine signatures for each individual. All the static signatures were generated with different modeled pens. The synthetic users have been generated following the procedure described at:

M. A. Ferrer, S. Chanda, M. Diaz, C. Kr. Banerjee, A. Majumdar, C. Carmona-Duarte, P. Acharya and U. Pal (2018). "Static and Dynamic Synthesis of Bengali and Devanagari Signatures", IEEE Transactions on Cybernetics, vol. 48, no. 10, pp. 2896-2907. doi: 10.1109/TCYB.2017.2751740

Moises Diaz, Sukalpa Chanda, Miguel Ferrer, Chayan Kr. Banerjee, Anirban Majumdar, Cristina Carmona-Duarte, Parikshit Acharya and Umapada Pal. "Multiple Generation of Bengali Static Signatures". 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), Shenzhen, China, 23-26 October 2016, pp. 42-47 doi: 10.1109/ICFHR.2016.0021

The Static signatures are in "jpg" format and equivalent resolution of 600 dpi. The files of the genuine signatures are named xxx/c-xxx-yy.jpg where xxx is the number of the signer and yy its repetition.

The dynamic signatures are in mat files which contains the x and y coordinates of the signature sampled at 100Hz along with the pressure p sequence. The files of the genuine signatures are named xxx/c-xxx-yy.mat where xxx is the number of the signer and yy its repetition.

For performance reference, please, see the table below which is a excerpt of the above mentioned reference

Training

Classifier

Bengali

Devanagari

Real dataset 1

Real dataset 2

Synthetic

Real dataset 1

Real dataset 2

Synthetic

2

HMM

6,03%

5,54%

6,24%

5,32%

6,71%

5,73%

SVM

4,32%

0,84%

2,73%

2,85%

1,37%

2,03%

DTW

NA

0,41%

1,66%

NA

0,41%

1,02%

Man

NA

8,19%

12,6%

NA

9,77%

12,2%

5

HMM

4,08%

3,18%

3,06%

3,37%

4,43%

2,68%

SVM

1,97%

0,23%

0,67%

1,56%

0,56%

0,47%

DTW

NA

0,27%

0,47%

NA

0,31%

0,49%

Man

NA

3,41%

6,50%

NA

4,16%

5,79%

8

HMM

3,17%

2,46%

2,38%

2,77%

3,58%

1,75%

SVM

1,32%

0,13%

0,34%

1,37%

0,38%

0,24%

DTW

NA

0,33%

0,25%

NA

0,34%

0,36%

Man

NA

2,7%

5,44%

NA

3,59%

4,7%

10

HMM

2,5%

2,34%

1,71%

2,77%

3,22%

1,34%

SVM

1,12%

0,13%

0,25%

1,33%

0,31%

0,14%

DTW

NA

0,28%

0,23%

NA

0,29%

0,38%

Man

NA

2,57%

5,11%

NA

3,11%

4,41%

NA stands for 'Not Apply' as dataset 1 does not contain online signatures

LICENSE AGREEMENT FOR NON-COMMERCIAL RESEARCH USE OF OffOnSyntheticBengaliSignatures and OffOnSyntheticDevanagariSignatures

 

Article to cite: M. A. Ferrer, S. Chanda, M. Diaz, C. Kr. Banerjee, A. Majumdar, C. Carmona-Duarte, P. Acharya and U. Pal (2018). "Static and Dynamic Synthesis of Bengali and Devanagari Signatures", IEEE Transactions on Cybernetics, vol. 48, no. 10, pp. 2896-2907. doi: 10.1109/TCYB.2017.2751740

@article{ferrer2017static, title={Static and dynamic synthesis of Bengali and Devanagari signatures}, author={Ferrer, Miguel A and Chanda, Sukalpa and Diaz, Moises and Banerjee, Chayan Kumar and Majumdar, Anirban and Carmona-Duarte, Cristina and Acharya, Parikshit and Pal, Umapada}, journal={IEEE transactions on cybernetics}, volume={48}, number={10}, pages={2896--2907}, year={2017}, publisher={IEEE} }

Article to cite: Moises Diaz, Sukalpa Chanda, Miguel Ferrer, Chayan Kr. Banerjee, Anirban Majumdar, Cristina Carmona-Duarte, Parikshit Acharya and Umapada Pal. "Multiple Generation of Bengali Static Signatures". 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), Shenzhen, China, 23-26 October 2016, pp. 42-47 doi: 10.1109/ICFHR.2016.0021

@inproceedings{diaz2016multiple, title={Multiple generation of Bengali static signatures}, author={Diaz, Moises and Chanda, Sukalpa and Ferrer, Miguel A and Banerjee, Chayan Kr and Majumdar, Anirban and Carmona-Duarte, Cristina and Acharya, Parikshit and Pal, Umapada}, booktitle={2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)}, pages={42--47}, year={2016}, organization={IEEE} }

 

 

 

 

 

 

GPDS Synthetic OnLine and OffLine Signature database

 

GPDSsyntheticOnLineOffLineSignature database

 

Dual Off line and On line signature database. It contains data from 10000 synthetic individuals: 24 genuine signatures for each individual, plus 30 forgeries of his/her signature. All the static signatures were generated with different modeled pens.

The synthetic users have been generated following the procedure described at:

 

M. A. Ferrer, M. Diaz, C. Carmona-Duarte, A. Morales, (2016), "A Behavioral Handwriting Model for Static and Dynamic Signature Synthesis", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1041-1053. doi: 10.1109/TPAMI.2016.2582167

 

The Static signatures are in "png" format and equivalent resolution of 600 dpi. The files of the genuine signatures are named xxx\c-xxx-yy.png and the files of the forgeries are named xxx\cf-xxx-yy.png where xxx is the number of the signer and yy its repetition.

 

The dynamic signatures are in \93mat\94 files which contains the x and y coordinates of the signature sampled at 100Hz along with the pressure p sequence. The files of the genuine signatures are named xxx\c-xxx-yy.mat and the files of the forgeries are named xxx\cf-xxx-yy.mat where xxx is the number of the signer and yy its repetition.

 

For performance reference, the verifiers are trained by following the same well-established experimental protocol as in [2] in which the training set consists of 5 randomly selected genuine signatures. The remaining genuine signatures are used for testing the false rejection rate. The false acceptance rate for the random impostor experiment has been obtained with the genuine test samples from all the remaining users whereas the false acceptance rate for the deliberate forgeries experiment has been worked out with the forgery samples of each signer.

 

The selected classifiers are four: two off-line and two on-line. The first off-line is based on Hidden Markov Model (HMM) and geometrical features [1]. The second one is a Support Vector Machine (SVM) fed with Local Binary Pattern (LBP) features [2] The first on-line classifier is based on Dynamic Time Warping (DTW) [3] with Euclidean distance and parameter vector given by [x, y, p, dx, dy, dp, ddx, ddy, ddp] where x and y are the sample position and p the pressure (if it is not available in the real dataset is set to 0 for pen-ups and 1 for the rest of samples). The last online classifier [4] is based on Manhattan distance between histograms of different absolute and relative measures of the signature to compare.

1.                 M. A. Ferrer, J. B. Alonso and C. M. Travieso, "Offline Geometric Parameters for Automatic Signature Verification using Fixed-Point Arithmetic", in IEEE Transactions on pattern analysis and machine intelligence, vol. 27, no. 6, pp. 993-997, June 2005.

2.                 M. A. Ferrer, F. Vargas, A. Morales and A. Ordoñez, "Robustness of Off-line Signature Verification Based on Gray Level Features", in IEEE Transactions on Information Forensics and Security, vol. 7, no. 3, pp. 966-977, June 2012.

3.                 A. Fischer, M. Diaz-Cabrera, R. Plamondon, M. A. Ferrer, "Robust Score Normalization for DTW-Based On-Line Signature Verification", in 13th International Conference on Document Analysis and Recognition, Tunis, Tunisia, August, 2015.

4.                 N. Sae-Bae and N. Memon, \93Online Signature Verification on Mobile Devices\94 in IEEE Transactions on Information Forensics and Security, vol. 9, no. 6, pp. 933\96947, June 2014

The results in terms of EER in % are the next

 

 

users

Random Impostors\92 experiment

Deliberate Forgeries\92 experiment

Static

experiments

Dynamic

experiment

Static

experiments

Dynamic

experiment

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

150

4,17

1,31

0,53

2,13

11,48

16,45

4,59

3,00

300

4,32

1,45

0,43

1,83

12,11

16,5

4,32

2,53

1000

4,37

1,63

0,44

1,91

11,07

17,01

5,09

2,96

2000

4,44

1,73

0,49

1,98

11,34

16,63

5,29

2,95

5000

4,53

1,63

0,54

2,01

11,1

16,93

5,25

2,94

10000

4,81

1,42

0,52

2,07

13,8

18,95

5,24

2,99

 LICENSE AGREEMENT FOR NON-COMMERCIAL RESEARCH USE OF GPDSsyntheticOnLineOffLineSignature CORPUS

Article to cite: Ferrer, M. A., Diaz, M., Carmona-Duarte, C., & Morales, A. (2016). A behavioral handwriting model for static and dynamic signature synthesis. IEEE transactions on pattern analysis and machine intelligence, 39(6), 1041-1053. doi: 10.1109/TPAMI.2016.2582167

@article{ferrer2016behavioral, title={A behavioral handwriting model for static and dynamic signature synthesis}, author={Ferrer, Miguel A and Diaz, Moises and Carmona-Duarte, Cristina and Morales, Aythami}, journal={IEEE transactions on pattern analysis and machine intelligence}, volume={39}, number={6}, pages={1041--1053}, year={2016}, publisher={IEEE} }

Article to cite: Ferrer, M. A., Diaz-Cabrera, M., & Morales, A. (2014). Static signature synthesis: A neuromotor inspired approach for biometrics. IEEE Transactions on pattern analysis and machine intelligence, 37(3), 667-680. doi: 10.1109/TPAMI.2014.2343981

@article{ferrer2014static, title={Static signature synthesis: A neuromotor inspired approach for biometrics}, author={Ferrer, Miguel A and Diaz-Cabrera, Moises and Morales, Aythami}, journal={IEEE Transactions on pattern analysis and machine intelligence}, volume={37}, number={3}, pages={667--680}, year={2014}, publisher={IEEE} }

 

 

 

 

 

GPDS Synthetic Signature database

 

GPDSsyntheticSignature database

 

Off line signature database. It contains data from 4000 synthetic individuals: 24 genuine signatures for each individual, plus 30 forgeries of his/her signature. All the signatures were generated with different modeled pens. This database replaces previous signatures databases.

 

The synthetic users have been generated following the procedure described at:

Miguel A. Ferrer, Mois\E9s Diaz-Cabrera, Aythami Morales, "Static Signature Synthesis: A Neuromotor Inspired Approach for Biometrics," IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN: 0162-8828, vol. 37, no. 3, pp.1- 667-680, March 2015.

The signatures are in "jpg" format and equivalent resolution of 600 dpi. The files of the genuine signatures are named xxx\c-xxx-yy.png and the files of the forgeries are named xxx\cf-xxx-yy.png where xxx is the number of the signer and yy its repetition.

 

For performance reference, our results with previous public databases using the verifier used at:

 

    Miguel A. Ferrer, Francisco Vargas, Aythami Morales, Aaron Ordo\F1ez, "Robustness of Off-line Signature Verification based on Gray Level      Features", IEEE Transactions on Information Forensics and Security, vol. 7, no. 3, pp. 966-977, June 2012.

 

Are the next

Training 10 samples

Random Forgeries

Simulated Forgeries

Database

Users

MCYT

75

0.35 %

11.54 %

GPDS960

75

0.37%

13.78%

GPDS960

150

0.44%

15.90%

GPDS960

881

0.88%

23.42%

 

The results with the synthetic database are the next, training with 10 genuine samples

Number of users

Random Forgeries

Simulated Forgeries

75

0.76%

16.01%

150

0.75%

15.08%

881

0.63%

15.19%

1500

0.79%

15.13%

2500

0.74%

15.89%

4000

0.79%

16.44%

 LICENSE AGREEMENT FOR NON-COMMERCIAL RESEARCH USE OF GPDSsynthetic Signature CORPUS

Article to cite: Ferrer, M. A., Diaz-Cabrera, M., & Morales, A. (2014). Static signature synthesis: A neuromotor inspired approach for biometrics. IEEE Transactions on pattern analysis and machine intelligence, 37(3), 667-680. doi: 10.1109/TPAMI.2014.2343981

@article{ferrer2014static, title={Static signature synthesis: A neuromotor inspired approach for biometrics}, author={Ferrer, Miguel A and Diaz-Cabrera, Moises and Morales, Aythami}, journal={IEEE Transactions on pattern analysis and machine intelligence}, volume={37}, number={3}, pages={667--680}, year={2014}, publisher={IEEE} }

Article to cite: Ferrer, M. A., Diaz-Cabrera, M., & Morales, A. (2013, June). Synthetic off-line signature image generation. In 2013 International conference on biometrics (ICB) (pp. 1-7). IEEE. doi: 10.1109/ICB.2013.6612969

@inproceedings{ferrer2013synthetic, title={Synthetic off-line signature image generation}, author={Ferrer, Miguel A and Diaz-Cabrera, Moises and Morales, Aythami}, booktitle={2013 International conference on biometrics (ICB)}, pages={1--7}, year={2013}, organization={IEEE} }

 

 

 

 

 

gpds HAND

GPDS150hand database

 

The database consists of 10 different acquisitions of 150 people by a desk scanner. The 1500 images have been taken from the users\92 right hand. The user in our system can place the hand palm freely over the scanning surface; pegs, templates or any other annoying method for the users to capture their hands are not used. The hand contour with landmarks (valleys and tips of the fingers) and the segmented palms are also provided. They have been obtained automatically without supervision as described in the article to cite.

The signatures are in "jpg" format, 256 gray levels and 120 dpi of resolution. The files of the hands are named xxx\manoxxx_yy.jpg where xxx is the number of the signer and yy its repetition. The palm images are given in the files named xxx\palmaxxx_yy.jpg. The hand contour and landmarks are given in the Matlab2007 file metadata.mat. How to use these files can be seen in the file ReadDatabase.m.

LICENSE AGREEMENT FOR NON-COMMERCIAL RESEARCH USE OF GPDS150hand CORPUS

Article to cite: Ferrer, M. A., Morales, A., Travieso, C. M., & Alonso, J. B. (2007, October). Low cost multimodal biometric identification system based on hand geometry, palm and finger print texture. In 2007 41st annual IEEE international Carnahan conference on security technology (pp. 52-58). IEEE. doi: 10.1109/CCST.2007.4373467.

@inproceedings{ferrer2007low, title={Low cost multimodal biometric identification system based on hand geometry, palm and finger print texture}, author={Ferrer, Miguel A and Morales, Aythami and Travieso, Carlos M and Alonso, Jesws B}, booktitle={2007 41st annual IEEE international Carnahan conference on security technology}, pages={52--58}, year={2007}, organization={IEEE} }

 

 

 

 

 

 

 

GPDS hand 3 Band

GPDS100hand3Band database

 

Our hands database consists of 10 times 3 acquisitions (visible, 850nm and 1470nm bands) from 100 people. The 3000 images were taken from the users\92 right hand. Most of the users are between 23 to 40 years old. Approximately half of the database volunteers are male. The user in our system can place the hand palm freely over the plate; pegs, templates or any other annoying method for the users to capture their hands are not used. The cameras acquire the hand dorsum image. The image in the 1470nm band is acquired by a XENICS camera XEVA 1.7-320 with an InGaAs sensor, sensitive from 900 to 1700nm, with a band pass filter lens centered on 1470nm and bandwidth of 250nm. The image in the visible band is acquired with a color webcam quickcam E2500, with a resolution of 640x480 pixels. The image in the NIR band is acquired with a color webcam quickcam E2500, with a resolution of 640x480 pixels and a high pass filter lens with cutoff wavelength at 850nm. The procedure is described in the article to cite.

The signatures are in "bmp" format as given by the cameras. The files of the hands are named yyy\xxx_yyy_rr.bmp where xxx is the band (xxx= \91vis\92, 850 or 1450), yyy is the user number and rr its repetition.

 

LICENSE AGREEMENT FOR NON-COMMERCIAL RESEARCH USE OF GPDS hand 3 band

Article to cite: Ferrer, M. A., & Morales, A. (2011). Hand-shape biometrics combining the visible and short-wave infrared bands. IEEE transactions on information forensics and security, 6(4), 1305-1314. doi: 10.1109/TIFS.2011.2162948.

@article{ferrer2011hand, title={Hand-shape biometrics combining the visible and short-wave infrared bands}, author={Ferrer, Miguel A and Morales, Aythami}, journal={IEEE transactions on information forensics and security}, volume={6}, number={4}, pages={1305--1314}, year={2011}, publisher={IEEE} }

 

 

 

 

 

 

 

GPDS contactless hands

GPDS100Contactlesshands2Band database

 

Our contactless hands database consists of 10 times 2 acquisitions (visible and 850nm) from 100 people. The 2000 images were taken from the users' right hand. Most of the users are between 23 to 40 years old. Approximately half of the database volunteers are male. The user places his or her hand over the camera and touchless adjusts the position and pose of the hand in order to overlap with the hand mask drawn on the device screen. When the hand and mask overlap more than 70%, the device automatically acquires both the IR and visible image. Detail can be seen in the article to cite.

The acquisition device used consists of two inexpensive, standard web cams that obtain images of the hand at the same time. The so called infrared (IR) webcam acquires images in the infrared band (750 to 1000nm) and the so called visible (V) camera acquires images in the visible range (400 to 700nm). The IR webcam was created by simply taking out the webcam lens that eliminates the infrared radiation and adding a filter that eliminates the visible band. We used Kodak filter No 87 FS4-518 and No 87c FS4-519 with no transmittance below 750 nm.

The infrared illumination is composed of a set of 24 GaAs infrared emitting diode (CQY 99) with a peak wavelength emis-sion of 925 nm and a spectral bandwidth of 40 nm. The diodes were placed in an inverted U shape with the IR and V webcams in the middle. The open part of the U shape will coincide with the wrists of the hand image. The focus of the IR webcam lens is adjusted manually the first time the webcam is used.

 

The signatures are in "bmp" format as given by the webcam. The files of the hands in visible band and infrared band are named xxx\visible_xxx_yy.bmp and xxx\Infraro_xxx_yy.bmp where xxx is the number of the signer and yy its repetition. The segmented palm images are given in the files named xxx\palma_xxx_yy.jpg and the contour of the visible image is given in the files xxx\Icontvisible_xxx_yy. How to use these files can be seen in the file ReadDatabase.m.

 

LICENSE AGREEMENT FOR NON-COMMERCIAL RESEARCH USE OF GPDS Contactless hands

Article to cite: Ferrer, M. A., Vargas, F., & Morales, A. (2011, May). BiSpectral contactless hand based biometric system. In CONATEL 2011 (pp. 1-6). IEEE. doi: 10.5772/18096

@inproceedings{ferrer2011bispectral, title={BiSpectral contactless hand based biometric system}, author={Ferrer, Miguel A and Vargas, Francisco and Morales, Aythami}, booktitle={CONATEL 2011}, pages={1--6}, year={2011}, organization={IEEE} }

 

 

 

 

 

 

GPDS handSWIR hyperspectral database

Sample of GPDShandSWIRhyperspectral database

 

The GPDShandsSWIRhyperspectral database consists of 10 different samples from154 people. Each sample is composed of 350 images acquired by the hyperspectral device, so the total number of images on the database is 154\D710\D7350=539000 images in 256 bands between 900nm and 1600nm. The users were allowed to wear wristwatches or wristlets. The age of the users ranged from 18 to 60 years, 86% of them between 18 and 30. Approximately 70% are students and teachers from our university and the remaining 30% are administration and cleaning staff. In the database, 64% of the users are male. For the first 24 users, we acquired the hyperspectral images from each hand side, palm and dorsum.

 

More information can be found at the article to cite.

 

The acquisition device used consist on a Xenics\AE Xeva-1.7-320 camera which is based on an InGaAs detector, sensitive from 900 to 1700nm. The camera provides 256 gray level images with a resolution of 320 by 256 pixels. We used this in conjunction with a SPECIM\AE Imspector N17E optical spectrograph with numerical aperture f/2.0. This transforms the SWIR camera into a line spectral imaging device, as seen in Figure 2 which shows the reflectance along a longitudinal line (x axis) that crosses four fingers. The spectrographic images consist of 320 pixels in the x axis and 256 bands in the wavelength axis. As the aperture is f/2.0 and the distance from the lens to the plate is 41 cm, the x dimension covered is 21 cm. Therefore the horizontal resolution is approximately 38 dpi.

To add the information for the y axis, a rotating mirror scanner is attached to the objective lens of the spectrograph. As the mirror scanner rotates through 40\BA, the y spatial dimension covered is 29 cm.

The acquisition procedure is as follows. The user is asked to place the hand dorsum over the plate with the hand palm up; we therefore acquire the hyperspectral image of the hand palm. Once the hand is still, the mirror starts to rotate from minus 20 to plus 20 degrees. The camera acquires around 12 pictures per second. As the procedure takes 30 seconds approximately, we have 350 images in the y axis, with a vertical resolution of 30 dpi approximately. A reconstruction stage makes the hyperspectral cube a coherent object.

The XEVA 1.7-320 had a pixel operability of 99%. It produced 298 erroneous pixels the values of which we interpolated as the mean of the content of the pixels immediately above and below. We did not use the average of all the surrounding pixels because of row contiguous pixel errors.

The illumination system consists of two tungsten filament bulbs with a radiation spectrum from 400 to 1600nm. Most of the bulb energy is centered in the NIR. As can be seen in Figure 2, the bulbs are situated at approximately the same distance from each side of the hand, to avoid shadows or unbalanced lighting.

 

LICENSE AGREEMENT FOR NON-COMMERCIAL RESEARCH USE OF GPDShandSWIRhyperspectral database

Article to cite: Ferrer, M. A., Morales, A., & Díaz, A. (2014). An approach to SWIR hyperspectral hand biometrics. Information Sciences, 268, 3-19. doi: 10.1016/j.ins.2013.10.011

@article{ferrer2014approach, title={An approach to SWIR hyperspectral hand biometrics}, author={Ferrer, Miguel A and Morales, Aythami and D{\'\i}az, Alba}, journal={Information Sciences}, volume={268}, pages={3--19}, year={2014}, publisher={Elsevier} }

 

 

 

 

 

 

GPDS Veins

GPDS100VeinsCCDcylindrical database

 

The database consists of 10 different acquisitions of 102 people. The samples were acquired in two separated session one week: five the first time and other five samples the second session. The 1020 images have been taken from the users' right hand. The system to capture near images of the hand dorsum consists of two arrays of 64 LEDs in the band of 850nm, a CCD gigabit Ethernet PULNIX TM3275 camera with a high pass IR filter with 750nm as cutoff frequency, and a handle with two pegs for positional reference as described in the article to cite.

 

The signatures are in "bmp" format as given by the camera. The files of the hand veins are named xxx\mano-xxx-yyy.bmp where xxx is the number of the signer and yyy its repetition. A readdatabase.m file is provided.

 

 

GPDS100VeinsCMOScylindrical database

 

The database consists of 10 different acquisitions of 103 people. The samples were acquired in two separated session one week: five the first time and other five samples the second session. The 1030 images have been taken from the users' right hand. The system to capture near infrared images of the hand dorsum consists of two arrays of 64 LEDs in the band of 850nm and a CMOS webcam with a high pass IR filter with 750nm as cutoff frequency, and a cylindrical handle with two pegs for positional reference.

 

The users of VeinsCMOScylindrical and CMOSergonimic database are the same.

 

The signatures are in "bmp" format as given by the camera. The files of the hand veins are named xxx\venas_xxx_yy.bmp where xxx is the number of the signer and yy its repetition. A readdatabase.m file is provided.

 

 

GPDS100VeinsCMOSergonomic database

 

The database consists of 10 different acquisitions of 103 people. The samples were acquired in two separated session one week: five the first time and other five samples the second session. The 1030 images have been taken from the users\92 right hand. The system to capture near infrared images of the hand dorsum consists of two arrays of 64 LEDs in the band of 850nm and a CMOS webcam with a high pass IR filter with 750nm as cutoff frequency, and an ergonomic handle which fix the hand position in a suitable way for the user.

 

The users of VeinsCMOScylindrical and CMOSergonimic database are the same.

 

The signatures are in "bmp" format as given by the camera. The files of the hand veins are named xxx\venas_xxx_yy.bmp where xxx is the number of the signer and class=SpellE>yy its repetition. A readdatabase.m file is provided.

 

LICENSE AGREEMENT FOR NON-COMMERCIAL RESEARCH USE OF GPDS Veins

Article to cite: Ferrer, M. A., Morales, A., & Ortega, L. (2009). Infrared hand dorsum images for identification. Electronics letters, 45(6), 306-308. doi: 10.1049/el.2009.0136

@article{ferrer2009infrared, title={Infrared hand dorsum images for identification}, author={Ferrer, Miguel A and Morales, Aythami and Ortega, Lourdes}, journal={Electronics letters}, volume={45}, number={6}, pages={306--308}, year={2009}, publisher={IET} }

 

 

 

 

 

 

GPDS Lips

GPDS50Lips database

 

The database consists of 10 different acquisitions of the face of 51 people looking for highlight his/her lips with a CCD camera as described in:

 

Carlos. M. Travieso, J. Zhang, P- Miller, Jes\FAs B. Alonso, Miguel A. Ferrer, \93Bimodal biometric verification based on face and lips\94, Neurocomputing, ISSN 0925-2312 vol. 74, pp. 2407-2410, 2nd June 2011

 

The pictures are in "jpg" format. The files are named xxx\labio-xx-yy.jpg where xx is the number of the signer and yy its repetition.

 

A file in Matlab to read the database is provided: ReadDatabase.m

 

 

 

 

 

 

 

gpds SIGNATURE

CORPORA

GPDS960signature database

 

 

Unfortunately, this database is no longer available due to the The General Data Protection Regulation (EU) 2016/679 ("GDPR"). Instead, you can apply for the offline GPDSsyntheticSignature database or the GPDSsyntheticOnLineOffLineSignature database. Sorry for the inconvenience.

 

Off line signature database. It contains data from 960 individuals: 24 genuine signatures for each individual, plus 30 forgeries of his/her signature. The 24 genuine specimens of each signer were collected in a single day writing sessions. The forgeries were produced from the static image of the genuine signature. Each forger was allowed to practice the signature for as long as s/he wishes. Each forger imitated 3 signatures of 5 signers in a single day writing session. The genuine signatures shown to each forger are chosen randomly from the 24 genuine ones. Therefore for each genuine signature there are 30 skilled forgeries made by 10 forgers from 10 different genuine specimens.

 

The signatures are in "bmp" format, in black and white and 300 dpi. The files of the genuine signatures are named xxx\c-xxx-yy.bmp and the files of the forgeries are named xxx\cf-xxx-yy.bmp where xxx is the number of the signer and yy its repetition

 

As the background of the scanned signatures is well contrasted with the darker signature strokes, the signature images where binarized by thresholding with a fixed threshold and  a sort of hair sticking out from signature strokes was eliminated [1]

 

A file in Matlab to read the database is provided: ReadDatabase.m

 

 

[1] M. Blumenstein, Miguel A. Ferrer, J.F. Vargas, \93The 4NSigComp2010 off-line signature verification competition: Scenario 2\94, in proceedings of 12th International Conference on Frontiers in Handwriting Recognition, ISSBN: 978-0-7695-4221-8, pp. 721-726, Kolkata, India, 16-18 November 2010.

 

 

 

4NSigComp2010 Scenario 2

Off-line signature verification competition database

 

 

The off line signature verification competition held during the 12th International Conference on Frontiers in Handwriting Recognition (ICFHR 2010, Kolkata, India) used as database  a subset of the GPDS960Signature database.

As training subset, the 4NSigCom2010 used 4 genuine signatures of the individuals 301 to 700 of the GPDS960signature corpus. The files of the genuine signatures are named Trainingset\xxx\c-xxx-yy.bmp being xxx the id of the signer which goes from 301 to 700 and yy the repetition from 01 to 04

A Matlab scrip to read and display the train images is provided: ReadTrainingSignatures.m

For testing, 30000 questioned signature images obtained from the GPDS960signature database were used. The test data includes original signatures of GPDS960signature signers 301 to 700, simulated forgeries of each user and random signatures from users 701 to 960. The test files has been named c-xxxxx-yyy.bmp being xxxxx the number of file from 00001 to 30000 and yyy the id of the signer identity claimed from 301 to 700.

A Matlab scrip to read and display the trest images is provided: ReadTestSigantures.m

To evaluate your automatic signature verifier with the 4NSigComp2010 Scenario 2 database, a matlab program called EvaluateASV is provided. As it has been done, this program needs the program asv.m and the file \934nSigCompSignatureIdentification.mat\94 which contains the matrix called sign.

The program asv.m should be a matlab function defined as:

Function decision=asv(signature,id)

Where signature is the image of the signature to be verified and id is the identity claimed. Decision is supposed 1 if the signature is accepted as genuine and 0 if the signature is considered a forgery.

The mean of the sign matrix values are the next: The signature c-xxxx-yyy.bmp of the test set corresponds to the repetition sign(xxxx,2) of the signer sign(xxxx,1) in the GPDS 960Signature database; The yyy identity claimed by signature c-xxxx-yyy.bmp is equal to sign(xxxx,3). Finally, if sign(xxxx,4) is equal to 0 means that c-xxxx-yy.bmp is a genuine signature; if sign(xxxx,4) is equal to 1 means that c-xxxx-yyy.bmp is a imitation or simulated forgery while if sign(xxxx,4) is equal to 2 means that c-xxxx-yyy.bmp corresponds to random forgery.

For further information, please read [1].

[1] M. Blumenstein, Miguel A. Ferrer, J.F. Vargas, \93The 4NSigComp2010 off-line signature verification competition: Scenario 2\94, in proceedings of 12th International Conference on Frontiers in Handwriting Recognition, ISSBN: 978-0-7695-4221-8, pp. 721-726, Kolkata, India, 16-18 November 2010.

 

 

 

 

GPDS960GRAYsignature database

This database contains a gray level version of genuine signatures and imitations of the GPDS960Signature database.

 

Due to a move, unfortunately we lost the signatures of 79 users and 143 imitations of the remainder signers. So, the GPDS960GRAYsignature database consists of 881 users, 21144 genuine signatures and 26317 imitations. Total: 47485 signatures.

 

 The lost users and imitations are specified in the ReadDatabase.m file.

 

 In this case, the signatures are in "png" format and have been scanned at 600 dpi. The files of the genuine signatures are named xxx\c-xxx-yy.png and the files of the forgeries are named xxx\cf-xxx-yy.png where xxx is the number of the signer and yy its repetition.

 

 This version of the data base has been obtained scanning the sheets again at 600dpi. So, the segmentation errors are supposed different to those of GPDS960Signature database

 

 This database has been used in:

 

 Miguel A. Ferrer, Francisco Vargas, Aythami Morales, Aaron Ordo\F1ez, "Robustness of Off-line Signature Verification based on Gray Level Features", IEEE Transactions on Information Forensics and Security, vol. 7, no. 3, pp. 966-977, June 2012.

 

 

 

Checks database

 

This database contains 20 images: 12 bank checks and 8 invoices with varying degrees of background complexity.

 

This database has been used in the paper:

 

Miguel A. Ferrer, Francisco Vargas, Aythami Morales, Aaron Ordo\F1ez, "Robustness of Off-line Signature Verification based on Gray Level Features", IEEE Transactions on Information Forensics and Security, vol. 7, no. 3, pp. 966-977, June 2012.

 

Please, refer to it if you use this database.

 

The submitted paper blended the MCYT (http://atvs.ii.uam.es ) and GPDS960GRAYSignatures database with the check database to obtain a synthetic signature database with distorted gray levels. We used the multiply blend mode which multiplies the check image by the signature one. As we overlay gray level strokes, each stroke results in a new darker gray level.

 

The next Matlab files are provided:

 

1.      Read the check database divided in three gray level distortions as in the submitted paper: ReadCheckDatabase.m.

2.     Blend a signature with a given signature: BlendSignaturewithCheck.m

3.     As we have used texture parameters based on Local Binary Patterns (LB), Local Directional Pattern (LDP) and Local Derivative Pattern (LDerivP), the programs to work them out are also provided in the next Matlab files: LBP.m, LDP.m and LDeriv.m

 

CHECKS DATABASE

 

 

 

 

 

 

 

Latent Palmprint Identification Database

Latent Palmprint Identification Database

 

The latent palmprint identification database has been acquired under laboratory conditions. It is a unisession database and is composed of 380 latent palmprints from 100 different palms of 51 donors (28 maleand 23 female). The age of the donors range between 4 and 81 years old with several professional employments represented (manual workers, office workers, students). Each donor contributed with two impressions (right and left hands) and multiple latent prints which simulate realistic scenarios under different poses. These poses are based on actions such as: opening a door, pushing a chair, grasping a knife, leaning on a table, carrying objects of different weights, among others. For more details see the article to cite

LICENSE AGREEMENT FOR NON-COMMERCIAL RESEARCH USE OF Latent Palmprint Identification Database

 

Article to cite: Morales, A., Medina-Pérez, M. A., Ferrer, M. A., García-Borroto, M., & Robles, L. A. (2014, September). LPIDB v1. 0-Latent palmprint identification database. In IEEE International Joint Conference on Biometrics (pp. 1-6). IEEE. doi: 10.1109/BTAS.2014.6996268.

@inproceedings{morales2014lpidb, title={LPIDB v1. 0-Latent palmprint identification database}, author={Morales, Aythami and Medina-P{\'e}rez, Miguel A and Ferrer, Miguel A and Garc{\'\i}a-Borroto, Milton and Robles, Leopoldo Altamirano}, booktitle={IEEE International Joint Conference on Biometrics}, pages={1--6}, year={2014}, organization={IEEE} }

 

 

 

 

grupo de procesado digital de se\F1ales
Campus de Tafira, 35017 - Las Palmas
departamento de se\F1ales y comunicacionesPhone: +34 928 451 269   fax: +34 928 451 243
email: gpds@gi.ulpgc.es