Abstract
Video-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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aggarwal, G., Chowdhury, A., Chellappa, R.: A system identification approach for video-based face recognition. In: ICPR, pp. 175–178 (2004)
Lee, K., Ho, J., Yang, M., Kriegman, D.: Video-based face recognition using probabilistic appearance manifolds. In: CVPR, pp. 313–320
Hadid, A., Pietikäinen, M.: Combining appearance and motion for face and gender recognition from videos. PR 42(11), 2818–2827 (2009)
Mian, A.: Online learning from local features for video-based face recognition. PR 44(5), 1068–1075 (2011)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: CVPR, pp. 2169–2178 (2006)
Van De Sande, K., Gevers, T., Snoek, C.: Evaluating color descriptors for object and scene recognition. IEEE Trans. PAMI 32, 1582–1596 (2009)
Lowe, D.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)
Zhang, L., Chu, R., Xiang, S., Liao, S., Li, S.: Face detection based on multi-block lbp representation. Advances in Biometrics, 11–18 (2007)
Ahonen, T., Matas, J., He, C., Pietikäinen, M.: Rotation invariant image description with local binary pattern histogram fourier features. Image Analysis, 61–70 (2009)
Yang, J., Yu, K., Gong, Y., Huang, T.: Linear spatial pyramid matching using sparse coding for image classification. In: CVPR, pp. 1794–1801 (2009)
Lee, K., Ho, J., Yang, M., Kriegman, D.: Visual tracking and recognition using probabilistic appearance manifolds. CVIU 99, 303–331 (2005)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: CVPR, Intel, Microprocessor Research Labs, p. 511 (2001)
Carnegie, R.C.: Mean-shift blob tracking through scale space. In: CVPR, pp. 234–240 (2003)
Hsieh, C., Chang, K., Lin, C., Keerthi, S., Sundararajan, S.: A dual coordinate descent method for large-scale linear svm. In: ICML, pp. 408–415 (2008)
Chang, C., Lin, C.: Libsvm: a library for support vector machines (2001)
Vedaldi, A., Zisserman, A.: Efficient additive kernels via explicit feature maps. In: CVPR, pp. 3539–3546 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, C., Wang, Y., Zhang, Z. (2012). Patch-Based Bag of Features for Face Recognition in Videos. In: Zheng, WS., Sun, Z., Wang, Y., Chen, X., Yuen, P.C., Lai, J. (eds) Biometric Recognition. CCBR 2012. Lecture Notes in Computer Science, vol 7701. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35136-5_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-35136-5_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35135-8
Online ISBN: 978-3-642-35136-5
eBook Packages: Computer ScienceComputer Science (R0)