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.
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Wang, C., Wang, Y., Zhang, Z. (2012). Incremental Learning of Patch-Based Bag of Facial Words Representation for Online Face Recognition in Videos. In: Lin, W., et al. Advances in Multimedia Information Processing – PCM 2012. PCM 2012. Lecture Notes in Computer Science, vol 7674. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34778-8_1
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DOI: https://doi.org/10.1007/978-3-642-34778-8_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34777-1
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