Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Video-based Person Authentication Using the RBF Network
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
Discriminant Analysis of Principal Components for Face Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Deformation Analysis for 3D Face Matching
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
A Bayesian Similarity Measure for Direct Image Matching
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Face recognition under variable lighting using harmonic image exemplars
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Measuring the Similarity of Vector Fields Using Global Distributions
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Assessing the uniqueness and permanence of facial actions for use in biometric applications
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on recent advances in biometrics
Face matching and retrieval using soft biometrics
IEEE Transactions on Information Forensics and Security
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With the recent emphasis on homeland security, there is an increased interest in accurate and non-invasive techniques for face recognition. Most of the current techniques perform a structural analysis of facial features from still images. Recently, video-based techniques have also been developed but they suffer from low image-quality. In this paper, we propose a new method for face recognition, called Digital Image Skin Correlation (DISC), which is based on dynamic instead of static facial features. DISC tracks the motion of skin pores on the face during a facial expression and obtains a vector field that characterizes the deformation of the face. Since it is almost impossible to imitate another person's facial expressions these deformation fields are bound to be unique to an individual. To test the performance of our method in face recognition scenarios, we have conducted experiments where we presented individuals wearing heavy make-up as disguise to our DISC matching framework. The results show superior face recognition performance when compared to the popular PCA+ LDA method, which is based on still images.