Incremental learning of patch-based bag of facial words representation for online face recognition in videos

Authors:
Chao Wang;Yunhong Wang;Zhaoxiang Zhang
Affiliations:
School of Computer Science and Engineering, Beihang University, China;School of Computer Science and Engineering, Beihang University, China;School of Computer Science and Engineering, Beihang University, China
Venue:
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
Year:
2012

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Abstract

Video-based face recognition is a fundamental topic in image and video analysis, and presents various challenges and opportunities. In this paper, we introduce an incremental learning approach to video-based face recognition, which efficiently exploits the spatiotemporal information in videos. Face image sequences are incrementally clustered based on their descriptors. With the quantization of the facial words, representation of the face image is generated by concatenating the histograms from regions. In the online recognition, a temporal matrix and a voting algorithm are employed to judge a face video's identity. The proposed method achieves a 100% recognition rate performed on the Honda/UCSD database, and gives near realtime feedback. Experimental results demonstrate the effectiveness and flexibility of our proposed method.