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
Constructing Facial Identity Surfaces for Recognition
International Journal of Computer Vision
Probabilistic recognition of human faces from video
Computer Vision and Image Understanding - Special issue on Face recognition
Robust and Rapid Generation of Animated Faces from Video Images: A Model-Based Modeling Approach
International Journal of Computer Vision - Special Issue on Research at Microsoft Corporation
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Video-based face recognition: state of the art
CCBR'11 Proceedings of the 6th Chinese conference on Biometric recognition
Model-Based multi-view face construction and recognition in videos
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
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Face recognition from video has been extensively studied in recent years. Intuitively, video provides more information than a single image. But problems such as variation in pose and occlusion still remain. When a face is partially occluded, handling the occluded part of the face is an especially challenging task. In this paper, we propose a novel method to recognize a face from video based on face patches. First, face patches are cropped from the video frame by frame. Then, face patches are matched to an overall face model and stitched together. By accumulating the patches, a reconstructed face is built which is used in recognition. We test our method in two experiments. In the first experiment, a still face database is used by randomly occluding parts of the face and using the remaining face patches in recognition. The experiments show that our method achieves a comparable recognition rate with the recognition rate from the whole face image. In the second experiment, the method is tested on video sequences. We reach a recognition rate of 81%, while there is still missing data in the reconstructed face.