Sum Versus Vote Fusion in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminant Analysis of Principal Components for Face Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Face Pose Discrimination Using Support Vector Machines (SVM)
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Handbook of Face Recognition
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
SVM-based feature extraction for face recognition
Pattern Recognition
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Due to vast variations of extrinsic and intrinsic imaging conditions, face recognition remained to be a challenging computer vision problem even today. This is particularly true when the passive imaging approach is considered for robust applications. To advance existing recognition systems for face, numerous techniques and methods have been proposed to overcome the almost inevitable performance degradation due to external factors such as pose, expression, occlusion, and illumination. In particular, the recent part-based method has provided noticeable room for verification performance improvement based on the localized features which have good tolerance to variation of external conditions. The part-based method, however, does not really stretch the performance without incorporation of global information from the holistic method. In view of the need to fuse the local information and the global information in an adaptive manner for reliable recognition, in this paper we investigate whether such external factors can be explicitly estimated and be used to boost the verification performance during fusion of the holistic and part-based methods. Our empirical evaluations show noticeable performance improvement adopting the proposed method.