Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Polynomial Correlation Filters for Human Face Recognition
ICMLA '12 Proceedings of the 2012 11th International Conference on Machine Learning and Applications - Volume 01
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Correlation filters have shown good performance results for distortion tolerant applications especially in target and face recognition problems. In this paper, we investigate the performance of these filters when applied to partially occluded human faces. We present a system for eye region recognition based on a special class of unconstrained correlation filters called optimal trade off Maximum Average Correlation Height (OT-MACH) filter. This system is useful for people who cover their faces, due to, for example, diseases or cultural reasons. The performance of this system is evaluated using the extended Yale B dataset. Our experimental results show that this system is robust to occlusion compared to the principal component analysis (PCA) and the local binary pattern (LBP). The OT-MACH filter shows error rates of 0.31% and 10.31% for non-occluded and occluded face recognition systems, respectively.