IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Face Recognition by Support Vector Machines
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Person identification from heavily occluded face images
Proceedings of the 2004 ACM symposium on Applied computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Random Sampling for Subspace Face Recognition
International Journal of Computer Vision
Bayesian face recognition using support vector machine and face clustering
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Occlusion invariant face recognition using selective LNMF basis images
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
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Face recognition has always been a challenging task in reallife surveillance videos, with partial occlusion being one of the key factors affecting the robustness of face recognition systems. Previous researches had approached the problem of face recognition with partial occlusions by dividing a face image into local patches, and training an independent classifier for each local patch. The final recognition result was then decided by integrating the results of all local patch classifiers. Such a local approach, however, ignored all the crucial distinguishing information presented in the global holistic faces. Instead of using only local patch classifiers, this paper presents a novel multi-level supporting scheme which incorporates patch classifiers at multiple levels, including both the global holistic face and local face patches at different levels. This supporting scheme employs a novel criteria-based class candidates selection process. This selection process preserves more class candidates for consideration as the final recognition results when there are conflicts between patch classifiers, while enables a fast decision making when most of the classifiers conclude to the same set of class candidates. All the patch classifiers will contribute their supports to each selected class candidate. The support of each classifier is defined as a simple distance-based likelihood ratio, which effectively enhances the effect of a "more-confident" classifier. The proposed supporting scheme is evaluated using the AR face database which contains faces with different facial expressions and face occlusions in real scenarios. Experimental results show that the proposed supporting scheme gives a high recognition rate, and outperforms other existing methods.