Contrast limited adaptive histogram equalization
Graphics gems IV
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiclass learning, boosting, and error-correcting codes
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Support Vector Machines
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Face recognition: component-based versus global approaches
Computer Vision and Image Understanding - Special issue on Face recognition
Face Class Code Based Feature Extraction for Face Recognition
AUTOID '05 Proceedings of the Fourth IEEE Workshop on Automatic Identification Advanced Technologies
Journal of Cognitive Neuroscience
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Face recognition vendor test 2002 performance metrics
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Polymorphous facial trait code
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
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We propose the Facial Trait Code (FTC) to encode human facial images. The proposed FTC is motivated by the discovery of the basic types of local facial features, called facial trait bases , which can be extracted from a large number of faces. In addition, the fusion of these facial trait bases can accurately capture the appearance of a face. Extraction of the facial trait bases involves clustering and boosting approaches, leading to the best discrimination of the human faces. The extracted facial trait bases are symbolized and make up the n-ary facial trait codes. A given face can be then encoded at the patches specified by the traits to render an n-ary facial trait code with each symbol in its codeword corresponding to the closest trait base. We applied FTC to a typical face identification problem, and it yielded satisfactory results under different illumination conditions.