Institut Eurécom - BP 193 - F-06904 Sophia Antipolis cedex 1 Travaux actuels en reconnaissance faciale. Jean-Luc DUGELAY EURECOM, Sophia Antipolis. Réunion GdR SCATI du 08 décembre 2011 Systèmes biométriques 20/02/2012 - - p 1 Outline Introduction l’apprentissage semi supervisé ou co-entraîné des reconnaisseurs ; la robustesse des techniques de reconnaissance en présence d’occlusions ; la reconnaissance de visages à partir de vidéo ; la reconnaissance de visages à partir de techniques 3D ; la sécurité des techniques de reconnaissance face au leurrage ; les biométries faciales dites douces : genre, ethnicité, âge, etc. ; Conclusion
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Travaux actuels en reconnaissance faciale. · Travaux actuels en reconnaissance faciale. Jean-Luc DUGELAY EURECOM, Sophia Antipolis. Réunion GdR SCATI du 08 décembre 2011 Systèmes
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� Given:� A labeled set Dl and an unlabeled set Du;
� Initialization:� Learn LDA transform with Dl, create a template for each subject by the projected mean of the same class: � Create a template for each subject as the mean of LBP vectors of the same class;
� Iterative co-training: � LDA recognition: Project Du into LDA space, for each class, find the nearest sample to the template, remove it from Du and
add to Dl;� LBP recognition: In Du, for each class, find the nearest sample to the template, remove it from Du and add it to Dl;� LDA updating: Re-training the LDA projection matrix with the new Dl, and re-create the templates;� LBP updating: Re-create the templates.� Iterate untill the Du is empty;
- p 19
LDA
Unlabeled data
Test data
Labeled data
LBP
Results
� Starting from 2 labeled examples per subject
� The improvement of accuracy as a function of iterat ion
� Face recognition is a very attractive biometric but yet not as reliable as some other ones like fingerprints…
� Existing solutions in face recognition are still image-based (i.e. appearance only) whereas current sensors are video (i.e. webcam, video surveillance)…
Adding a dimension…
� Face Identification from Video� From physical to behavioral
biometrics� Multimodal:
Appearance + motion (pose & expressions)
� Face Identification in 3D
Outline
� Introduction
� l’apprentissage semi supervisé ou co-entraîné des reconnaisseurs ;
� la robustesse des techniques de reconnaissance en présence d’occlusions ;
� la reconnaissance de visages à partir de vidéo ; � la reconnaissance de visages à partir de techniques 3D ;
� la sécurité des techniques de reconnaissance face au leurrage ;
� les biométries faciales dites douces : genre, ethnicité, âge, etc. ;
� Conclusion
� Matta, Federico;Dugelay, Jean-Luc Tomofaces: eigenfaces extended to videos of speakers ICASSP 2008, IEEE International Conférence on Acoustics, Speech, and Signal Processing, March 30 - April 4, 2008, Las Vegas, Nevada, USA
� Matta, Federico;Dugelay, Jean-Luc Video face recognition: a physiological and behavioural multimodal approach ICIP 2007, 14th IEEE International Conference on Image Processing, Sept. 16-19, 2007, San Antonio, pp VI-497-VI-500
� Ouaret, Mourad; Dantcheva, Antitza; Min, Rui; Daniel, Lionel; Dugelay, Jean-Luc BIOFACE, a biometric face demonstrator ACMMM 2010, ACM Multimedia 2010, October 25-29, 2010, Firenze, Italy , pp 1613-1616
� l’apprentissage semi supervisé ou co-entraîné des reconnaisseurs ;
� la reconnaissance de visages à partir de vidéo ;
� la robustesse des techniques de reconnaissance en présence d’occlusions ;
� la reconnaissance de visages à partir de techniques 3D ; � la sécurité des techniques de reconnaissance face au leurrage ;
� les biométries faciales dites douces : genre, ethnicité, âge, etc. ;
� Conclusion
� Erdogmus, Nesli; Dugelay, Jean-Luc Automatic extraction of facial interest points based on 2D and 3D data SPIE 2011, Electronic Imaging Conference on 3D Image Processing (3DIP) and Applications, Vol 7864, January 23-27, 2011, San Francisco.
� Erdogmus, Nesli, Etheve, Rémy; Dugelay, Jean-Luc Realistic and animatable face models for expression simulations in 3D SPIE 2010, Electronic Imaging Conference on 3D Image Processing (3DIP) and Applications, January 17-21, 2010, San Jose, California | Also published as "SPIE - The International Society for Optical Engineering", Vol. 7526, 2010
� publicly available large photo-impostor database containing photo images from 15 subjects which is constructed using a generic cheap webcam.
� collected in three sessions. The place and illumination conditions of each session are different as well.
Illustration of different photo-attacks
� for each subject in each session, the webcam is used to capture a series of their face images (with frame rate 20fps and 500 images for each subject).
Classification of Captured and Recaptured Images to Detect Photograph Spoofing
� l’apprentissage semi supervisé ou co-entraîné des reconnaisseurs ;
� la reconnaissance de visages à partir de vidéo ;
� la reconnaissance de visages à partir de techniques 3D ;
� la robustesse des techniques de reconnaissance en présence d’occlusions ;
� la sécurité des techniques de reconnaissance face au leurrage ;
� les biométries faciales dites douces : genre, ethnicité, âge, etc. ;� Conclusion
� Dantcheva, Antitza; Velardo, Carmelo; D'angelo, Angela; Dugelay, Jean-Luc Bag of soft biometrics for person identification : New trends and challenges Mutimedia Tools and Applications, Springer, October 2010 , pp 1-39
Soft Biometrics?
� Provide (biometrical) information about individual
� Lack distinctiveness and permanence
� Are not “expensive” to compute
� Do not require the cooperation of the individual
� Can be sensed from a distance
� Can be applied to unknown individuals.
� Can increase the system reliability
� Narrowing down the search within a limited group of candidate individuals
exponentially with the number of soft biometric traits (e.g. glasses, moustache, facial shapes,…)
λ \ µ 2 3 4 5 6 7
2 4 9 16 25 36 49
3 8 27 64 125 216 343
4 16 81 256 625 1296 2401
5 32 243 1024 3125 7776 16807
6 64 729 4096 15625 46656 117649
7 128 2187 16384 78125 279936 823543
λ
Extraction
82
Viola & Jones Face and features detector
� Glasses: line detection between the eyes
� Color face soft biometrics: ROI finding
and GMM color classification
� Beard and moustache: comparison of
color of ROI’s color with skin and hair
color
[1] P. Kakumanua, S. Makrogiannisa, and N. Bourbaki s, “A survey of skin-color modeling and detection methods ”, Pattern Recognition , vol. 40, issue 3, March 2007.
[2] M. Zhao, D. Sun, and H. He, “Hair-color Modeling and Head Detection,” in Proc. WCICA , 2008, pp.7773-7776.
[3] X. Jiang, M. Binkert, B. Achermann, and H. Bunk e, “Towards Detection of Glasses in Facial Images,” Pattern Analysis & Applications, Springer London, vol. 3, pp. 9-18, 2000.
� l’apprentissage semi supervisé ou co-entraîné des reconnaisseurs ;
� la reconnaissance de visages à partir de vidéo ;
� la reconnaissance de visages à partir de techniques 3D ;
� la robustesse des techniques de reconnaissance en présence d’occlusions ;
� la sécurité des techniques de reconnaissance face au leurrage ;
� les biométries faciales dites douces : genre, ethnicité, âge, etc. ;� Conclusion
� Dantcheva, Antitza; Dugelay, Jean-Luc Female facial aesthetics based on soft biometrics and photo-quality ICME 2011, IEEE International Conference for Multimedia and Expo, July 11-15, 2011, Barcelona, Spain
Soft biometrics for facial aesthetics
� Ratios of facial features and their locations
� Facial color soft biometrics
� Shapes of face and facial features
� Non-permanent traits and
� Expression.
Examples:
� Ratio (eye height / head length) f/a
� Ratio (head width / head length) b/a
� Eye make up
� Face shape
� Eye Brow shape
� Fullness of Lips
� Ratio (from top of head to nose / head length) (d+c)/a
� Presence of glasses
� Lipstick
� Skin goodness
� Hair Length / Style
� Ratio (from top of head to mouth / head length) (d+c+e)/a
� Ratio (from top of head to eye / head length) d/a
[4] S. Bhattacharya, R. Sukthankar, and M. Shah, “A framework for photo-quality assessment and enhancem ent based on visual aesthetics,” In Proc. Of ACM MM, 2010.
[5] A. K. Moorthy and A.C. Bovik, “A modular framewo rk for constructing blind universal quality indices ”, IEEE Signal ProcessingLetters, 2009.
[6] A. K. Moorthy and A.C. Bovik: “BIQI Software Rel ease”, http://live.ece.utexas.edu/research/quality/b iqi.zip, 2009.
[7] Z. Wang, H.R. Sheikh, and A.C. Bovik, “No-refere nce perceptual quality assessment of JPEG compresse d images”, in Proc. Of IEEE ICIP, 2002.
α
Examples:
� Image format
� Image Resolution
� JPEG quality measure [7]
� Illumination
� Zoomfactor
� Angle of face
� BIQI [5], [6]
� Left eye distance to middle of image or to mass point [4]
� Simple and objective aesthetics measures regarding the photograph
We used the 37 presented objective facial features
xi to construct a linear metric for facial aesthetics