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Patch-based Bag of Features for Face Recognition in Videos
Chao Wang; Yunhong Wang; Zhaoxiang Zhang
Conference NameChinese Conference on Biometric Recognition
Source PublicationCCBR 2012
Conference Date4-5 December 2012
Conference PlaceGuangzhou, China
AbstractVideo-based face recognition is a fundamental topic in image processing and video representation, and presents various challenges and opportunities. In this paper, we introduce an efficient patch-based bag of features (PBoF) method to video-based face recognition that plenty exploits the spatiotemporal information in videos, and does not make any assumptions about the pose, expressions or illumination of face. First, descriptors are used for feature extraction from patches, then with the quantization of a codebook, each descriptor is converted into code. Next, codes from each region are pooled together into a histogram. Finally, representation of the image is generated by concatenating the histograms from all regions, which is employed to do the categorization. In our experiments, 100% recognition rate is achieved on the Honda/UCSD database, which outperforms the state of the arts. And from the theoretical and experimental results, it can be derived that, when choosing a single descriptor and no prior knowledge about the data set and object is available, the dense SIFT with ScSPM is recommended. Experimental results demonstrate the effectiveness and flexibility of our proposed method.
KeywordFace Recognition Video-based Face Recognition Bag Of Feature Sparse Coding
Document Type会议论文
Corresponding AuthorZhaoxiang Zhang
Recommended Citation
GB/T 7714
Chao Wang,Yunhong Wang,Zhaoxiang Zhang. Patch-based Bag of Features for Face Recognition in Videos[C],2012.
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