Biography

Aythami Morales Moreno received his M.Sc. degree in Telecommunication Engineering in 2006 from Universidad de Las Palmas de Gran Canaria. He received his Ph.D degree from La Universidad de Las Palmas de Gran Canaria in 2011. He performs his research works in the Digital Signal Processing Group (GPDS) at Las Palmas de Gran Canaria University and he has performed research stays in the Biometric Research Laboratory at Michigan State University, the Biometric Research Center at Hong Kong Polytechnic University and the Biometric System Laboratory at University of Bologna. His research interests are focused on pattern recognition, computer vision, machine learning and biometrics signal processing. He is author of more than 30 scientific articles published in international journals and conferences. He has received awards from ULPGC, La Caja de Canarias, SPEGC, and COIT. He has participated in 7 National and European projects in collaboration with other universities and private entities such as UAM, UPM, EUPMt, Indra, Unión Fenosa, Soluziona,...

Publications

(Google Scholar Citations)

Journals:

M. Diaz-Cabrera, M. A. Ferrer, A. Morales, "Modeling the Lexical Morphology of Western Handwritten Signatures", PLoS ONE, 2015, vol 10 (4). [Download] 

J. Galbally, M. Diaz-Cabrera, M. A. Ferrer, M. Gomez-Barrero, A. Morales and J. Fierrez, "On-line Signature Recognition Through the Combinaion of Real Dynamic Data and Synthetically Generated Static Data", Pattern Recognition, 2015, vol. 48 (9), pp 2921-2934. [Download]

Miguel A. Ferrer, Moises Diaz-Cabrera, Aythami Morales. “Static Signature Synthesis: A Neuromotor Inspired Approach for Biometrics”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, Vol. 37 (3), pp. 667-680. [Download]

A. Morales, R. Cappelli, M. A. Ferrer, D. Maltoni, "Synthesis and Evaluation of High Resolution Hand-Prints", IEEE Transactions on Information Forensics and Security,  Vol. 9 (11), 2014, pp. 1922 - 1932. [Download]

Miguel A. Ferrer, Aythami Morales, Alba Diaz. “An Approach to SWIR Hyperspectral Hand Biometrics”. Information Science 2014, Vol. 268, pp. 3-19. [Download]

Marta Gomez-Marrero, Javier Galbally, Aythami Morales, Miguel A. Ferrer, Julian Fierrez,  Javier Ortega-Garcia. “A Novel Hand Reconstruction Approach and its Application to Vulnerability Assessment”. Information Science 2014, Vol. 268, pp. 103-121. [Download]

Miguel A. Ferrer, Aythami Morales, Carlos M. Traviesoa, Jesus B. Alonso. “Wide band spectroscopic skin detection for contactless hand biometrics”. IET Computer Vision 2012, Vol. 6, Issue 5, pp 415-424. [Download]

Miguel A. Ferrer, Jesus F. Vargas, Aythami Morales and Aarón D. Ordóñez. “Robustness of Off-line Signature Verification based on Gray Level Feratures”. IEEE Trans. Information Forensics & Security 2012, Vol. 7, No. 3, pp. 966-977. [Download]

A. Morales, E. González, M. A. Ferrer. “On the feasibility of interoperable schemes in hand biometrics”. Sensors 2012, Volume 12, Issue 2, pp. 1352-1382. [Download]

A. Morales, M. A. Ferrer and A. Kumar. “Toward Contactless Palmprint Authentication”. IET Computer Vision 2011, Volume 5, Issue 6, pp. 407-416. [Download]

Miguel A. Ferrer and Aythami Morales. “Hand-Shape Biometrics combining the visible and Short Wave InfraRed Bands”. IEEE Trans. Information Forensics & Security 2011, Vol. 6, No. 4, pp. 1305-1314. [Download]

A. Morales, C. M. Travieso, M. A. Ferrer, J. B. Alonso “Improved Finger Knuckle Print Authentication based on Orientation Enhancement”. Electronics Letters 2011, Volume 47, Issue 6, pp. 380-381. [Download]

M. A. Ferrer, A. Morales, J.B. Alonso. "Fingers shape biometric identification using Point Distribution Models". Electronics Letters 2010, Volume 46, Issue 7, pp. 495-497. [Download]

M. A. Ferrer, A. Morales, L. Ortega. "Infrared hand dorsum images for identification". Electronics Letters 2009, Volume 45, Issue 6, pp. 306-308. [Download]

Conferences and Book Chapters:

Miguel A. Ferrer, Moises Diaz-Cabrera, Aythami Morales, Javier Galbally, Marta Gomez-Barrero, "Realistic Synthetic Off-Line Signature Generation Based on Synthetic On-Line Data", 47th IEEE International Carnahan Conference on Security Technology, Medellin, 8-11 October 2013, pp. 116-121. [Download]

Aythami Morales, Miguel A. Ferrer, Moises Diaz-Cabrera, Esther Gonzalez, "Analysis of local descriptors features and its robustness applied to ear recognition", 47th IEEE International Carnahan Conference on Security Technology, Medellin, 8-11 October 2013, pp. 171-175. [Download]

Miguel A. Ferrer, Aythami Morales, Umapada Pal, "LBP Based Line-wise Script Identification", Proceedings of to the twelfth IAPR International Conference on Document Analysis and Recognition (ICDAR 2013), Washington DC, USA, August 23-29th, 2013, pp. 369-373.  [Download]

Miguel A. Ferrer, Moisés Díaz-Cabrera, Aythami Morales, "Synthetic Off-Line Signature Image Generation", 6th IAPR International Conference on Biometrics, Madrid, 4-7 June 2013, pp. -. [Download]

Marta Gomez-Barrero, Javier Galbally, Aythami Morales, Miguel A. Ferrer, Julian Fierrez, Javier Ortega-Garcia, "Inverse Biometrics: A Case Study in Hand Geometry Authentication",  21stInternational Conference on Pattern Recognition, Tsukuba, 11-15 November 2012, pp. 1281-1284. [Download]

Aythami Morales, Miguel A. Ferrer, Carlos M. Travieso, Jesus B. Alonso, "Multisampling approach for contactless hand biometrics", 46th IEEE International Carnahan Conference on Security Technology, Boston, 15-18 October 2012, pp. 224-229. [Download]

Miguel A. Ferrer, Aythami Morales, Jesus Francisco Vargas, Ivan Lemos and Monica Quintero. "Is It Possible to Automatically Identify Who Has Forged My Signature?: Approaching to the Identification of a Static Signature Forger". In Proceedings of 10th IAPR International Workshop on Document Analysis Systems, 27-29 March 2012, Gold Coast, Queensland, Australia, pp. 175-179. [Download]

A. Morales, A. Kumar y M. A. Ferrer.“Incorporating color for reliable palmprint authentication”. In Proceedings of International Conference on Image Processing, Brussels, Belgium, 11-14 September 2011, pp. 3254-3257. [Download]

Aythami Morales and Miguel A. Ferrer, “BiSpectral Contactless hand based biometric identification device”, Recent Application in Biometrics, Ed. InTech, 2011, pp. 267-284. [Download]

Ferrer, M.A.; Morales, A.; E. González, “Looking for Hand Shape based biometric devices interoperability”. In Proceedings of 45nd Annual IEEE International Carnahan Conference on Security Technology, Barcelona, Spain, 18-21 October 2011, pp 68-72. [Download]

Jiri Mekyska, Xavier Font-Aragones, Marcos Faundez-Zanuy, Ruben Hernandez-Mingorance, Aythami Morales-Moreno and Miguel Angel Ferrer-Ballester. ”Thermal hand image segmentation for biometric recognition”. In Proceedings of the 45nd Annual IEEE International Carnahan Conference on Security Technology, Barcelona, Spain, 18-21 October 2011, pp. 26-30. [Download]

M. A. Ferrer; A. Morales; E. González, C. M. Travieso, “Looking for hand biometrics interoperability”. In Proceedings of the International Conference on Hand-based Biometrics, Hong Kong, 17-18 November 2011, pp. 1-6. [Download]

M. A. Ferrer; J. F. Vargas; A. Morales, “BiSpectral Contactless hand based biometric identification device”. In Proceedings of the 2nd National Conference on Telecomunications, Arequipa, 2011, pp. 34-39. May 2011. [Download]

M. A. Ferrer; A. Morales; J. F. Vargas, “Off-line Signature Verification using Local Patterns”. In Proceedings of the 2nd National Conference on Telecomunications, Arequipa, 2011, pp. 74-79.  [Download]

A. Morales y M. A. Ferrer, “Contact-free hand biometrics system for real environments based on geometric features”. Recent Advanced in Signal Processing, Ed. In-Tech, pp. 185-194, 2010. [Download]

A. Morales, M. A. Ferrer y A. Kumar. “Improved Palmprint Authentication Using Contactless Imaging”. In Proceedings of the IEEE International Conference on Biometrics: Theory, Applications and Systems, Washington DC, pp. 1-6, Sep 2010. [Download]

Marcos Faundez-Zanuy, Joan Fàbregas, Miguel Ángel Ferrer-Ballester, Aythami Morales, Javier Ortega-Garcia, Guillermo Gonzalez de Rivera and Javier Garrido.”Biometric Database Acquisition Close to “Real World” Conditions”. Lecture Notes in Computer Science: Development of Multimodal Interfaces: Active Listening and Synchrony, Vol. 5967, pp. 197-206. 2010. [Download]

M. A. Ferrer, A. Morales, C. M. Travieso, J. B. Alonso, “Combining hand biometric traits for personal identification”. In Proceedings of 43nd Annual IEEE International Carnahan Conference on Security Technology. pp. 155-159, 2009. [Download]

Aythami Morales, Miguel Ángel Ferrer, Marcos Faundez, Joan Fàbregas, Guillermo Gonzalez, Javier Garrido, Ricardo Ribalda, Javier Ortega, Manuel Freire. “Biometric System Verification Close to "Real World" Conditions”. Biometric ID Management and Multimodal Communication, Joint COST 2101 and 2102 International Conference, BioID_MultiComm 2009, Madrid, Spain, September 16-18, 2009. [Download]

M. A. Ferrer, A. Morales, C. M. Travieso, J. B. Alonso, “Influence of the pegs number and distribution on a biometric device based on hand geometry”. In Proceedings of the 42nd Annual IEEE International Carnahan Conference on Security Technology, Prague, pp. 221 – 225, 2008. [Download]

A. Morales, M. A. Ferrer, C. M. Travieso, J. B. Alonso, “Sistema de autenticación sin contacto basado en la geometría de la mano para entornos operacionales”. IV Jornadas de Reconocimiento Biométrico de Personas (JRBP08). Valladolid. Septiembre 2008. [Download]

A. Morales, C. M. Travieso. Non-Cooperative Facial Biometric Identification Systems. Encyclopedia of Artificial Intelligence. Information Science Reference. 978-1-59904-849-9. Mayo 2008. [Download]

A. Morales, M. A. Ferrer, F. Díaz, J. B. Alonso, C. M. Travieso, “Contact-free hand biometric system for real environments”. In Proceedings of the European Signal Processing Conference, Laussane, 2008. [Download]

A. Morales, M. A. Ferrer, C. M. Travieso, J. B. Alonso, “Comparing infrared and visible illumination for contact-less hand based biometric scheme”. In Proceedings of the 42nd IEEE International Carnahan Conference on Security Technology, Prage, pp. 191-197, 2008. [Download]

A. Morales, M. A. Ferrer, P. Henríquez, C. M. Travieso, J. B. Alonso , “Multifeature Knuckles Parameterization”. In Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, Innsbruck, 2008. [Download]

A. Morales, M. A. Ferrer, C. M. Travieso, J. B. Alonso, “A knuckles texture verification method in a transformed domain” First Spanish Workshop in Biometrics. Girona Junio 2007. [Download]

M. A. Ferrer, A. Morales, C. M. Travieso, J. B. Alonso, “Low Cost Multimodal Biometric identification System Based on Hand Geometry, Palm and Finger Print Texture”. In Proceedings of the 41st Annual IEEE International Carnahan Conference on Security Technology, Otawa, pp. 52 – 58, 2007. [Download]

Projects

Research

Contactless Hand Biometrics:

The hand-based biometric identification have traditionally been based on peg guide or the recently explored more convenient peg-free hand imaging systems. Therefore the contact between user and device is inevitable. The deployment of such devices with large number of users, for example, airport access as during the earlier USVISIT program, raises hygienic concerns. The usage of contactless acquisition devices emerges as the obvious solution to alleviate such hygienic concerns. The absence of contact improves the user convenience but results in large image variations wich require robust algorithms to accommodate such variations.

The main difference between contact based and contactless systems lies in the significant intra-class variations resulting from the absence of any contact or guiding surface to restrict such variations. Such variations can result from the rotational and translation variations, projective distortion, scale variations and blurring due to the hand movement during the image acquisition. The first challenge is therefore to employ better image normalization while the key research challenge is to develop robust feature extraction and matching approaches which are invariant to such image variations from contactless imaging.

Fig. The images in first row are from two subjects in IITD touchless database while second and third row images are from two subjects in GPDS-CL1 and GPDS-CL2 database.

IDeTIC Biometric Identification Demo Video 

Liveness Detection:

The biometric recognition techniques work well, but they can be physically spoofed, i.e., to can accept as genuine a fake or imitated hand. Even a simple, printed hand picture is able to deceive the system. To alleviate this weakness, spoof detection techniques are used, which are based on intrinsic properties of a living body (density, elasticity, capacitance, etc.) or involuntary signals from living tissue, such as pulse, blood pressure, etc. These countermeasures come into effect when the hand (genuine or imitated) touches the device although recently has proposed a touchless antispoofing approach based on pulse oximetry. Other touchless spoof detectors have been developed in the visible band, which analyze the R-G-B components of a skin picture, statistical model for human skin or multispectral imaging.

Fig . Some examples of images acquired at 1470nm and in red bands. Upper row: red band; Lower row: 1470nm band. From left to right: genuine hand, clay, cork, plaster, black plastic and silicone imitated hands.

 IDeTIC Aliveness Detection Demo Video

Hand Biometrics Interoperability:

The interoperability is one of the aspects of the biometry that has been scarcely studied. This property give us a measure of the performance when you enroll a user with a biometric device A and verifies it with a biometric device B. Working with interoperable procedures allow the companies to upgrade their biometric devices without the cost of enrolling again all the users and reduces the technological dependences between users, models and systems.  

The proliferation of different vendors and research groups has been caused a widespread deployment of proposals.  The absence of common frameworks to compare in fair conditions each proposal is an important lack of the industry. The study of the interoperability between different sensors or approaches (dorsum and palm) allows to the researchers to find solutions with a low dependence of the acquisition systems employed. 

 Fig. Hand biometric recognition schemes for traditional and interoperable approaches

Available Databases (http://www.gpds.ulpgc.es/download/index.htm):

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’ 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:

Miguel A. Ferrer, Aythami Morales, Carlos M. Travieso, Jesús B. Alonso, “Low Cost Multimodal Biometric Identification System based on Hand Geometry, Palm and Finger Textures”, in 41st Annual IEEE International Carnahan Conference on Security Technology, ISBN: 1-4244-1129-7, pp. 52-58, Ottawa, Canada, 8-11 October 2007.

The images are in "jpg" format, 256 gray levels and 120 dpi of resolution. The files of the hands are named xxxmanoxxx_yy.jpg where xxx is the number of the signer and yy its repetition. The palm images are given in the files named xxxpalmaxxx_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.

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’ 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:

Miguel A. Ferrer, Aythami Morales, “Hand Shape Biometrics combining the visible and Short Wave In fraReds Bands”. IEEE Trans. Information Forensics & Security 2011, Vol. 6, No. 4, pp. 1305-1314.

The images are in "bmp" format as given by the cameras. The files of the hands are named yyyxxx_yyy_rr.bmp where xxx is the band (xxx= ‘vis’, 850 or 1450), yyy is the user number and rr its repetition.

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:

Miguel A. Ferrer, Francisco Vargas, Aythami Morales, “BiSpectral Contactless hand based biometric system”, in 2nd National Conference on Telecommunications (CONATEL 2011), Arequipa, Perú, 17-20 May, 2011.

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

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 infrared 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:

Miguel A. Ferrer, Aythami Morales, Lourdes Ortega, “Infrarred hand dorsum images for identification”, in Electronic Letters, vol 45, No 6, pp. 306-308, ISSN 0013-5194, 12 March 2009. 

The images are in "bmp" format as given by the camera. The files of the hand veins are named xxxmano-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.

Miguel A. Ferrer, Aythami Morales, Lourdes Ortega, “Infrarred hand dorsum images for identification”, in Electronic Letters, vol 45, No 6, pp. 306-308, ISSN 0013-5194, 12 March 2009. 

The images are in "bmp" format as given by the camera. The files of the hand veins are named xxxvenas_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’ 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.

Miguel A. Ferrer, Aythami Morales, Lourdes Ortega, “Infrarred hand dorsum images for identification”, in Electronic Letters, vol 45, No 6, pp. 306-308, ISSN 0013-5194, 12 March 2009.

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