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TOOLBOX |
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Novel Anthropomorphic Features For On-line Signatures |
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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}
}
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gpds Synthetic Duplicator Engine for Static Signatures |
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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}
}
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gpds HMM |
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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).
For any remarks about this toolbox, do not hesitate to contact the authors sending an e-mail to: gpds@gi.ulpgc.es
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Paper Referent |
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user's guide |
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DATABASE |
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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{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}
}
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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{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}
}
@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}
}
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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:
@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}
}
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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:
Please, cite our works if you find useful the database:
@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}
}
@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}
}
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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
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}
}
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GPDS Synthetic OnLine and OffLine Signature
database |
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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
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
@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}
}
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{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
@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 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{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{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 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
@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}
}
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