Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Based on Multiple Facial Features
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 Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Component-based Framework for Face Detection and Identification
International Journal of Computer Vision
Journal of Cognitive Neuroscience
Facial Trait Code and Its Application to Face Recognition
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Using component features for face recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
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The recently proposed Facial Trait Code (FTC) formulates the component-based face recognition problem as a coding problem using Error-Correcting Code The development of FTC is based on the extraction of local feature patterns from a large set of faces without significant variations in expression and illumination This paper reports a new type of FTC that encompasses the faces with large expression variation and under various illumination conditions We assume that if the patches of a local feature on two different faces look similar in appearance, this pair of patches will also show similar visual patterns when both faces change expressions and are under different illumination conditions With this assumption, we propose the Polymorphous Facial Trait Code for face recognition under illumination and expression variations The proposed method outperforms the original Facial Trait Code substantially in solving a strict face verification problem, in which only one facial image per individual is available for enrolling to the gallery set, and the probe set consists of facial images with strong illumination and expression variations.