Face Occlusion Detection Using Skin Color Ratio and LBP Features for Intelligent Video Surveillance Systems
Pengfei Ji, Yonghwa Kim, Yong Yang, Yoo-Sung Kim
Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 253–259 (2016)
Abstract. A face occlusion detection scheme which combines skin color ratio (SCR) and Local Binary Pattern (LBP) feature, is proposed. The proposed method mainly consists of four steps: foreground extraction, head detection, feature extraction, and occlusion detection. First, foreground is extracted by codebook background subtraction algorithm. Then, the head region is located using HOG head detector. After that, the skin-color ratio and LBP features are extracted, respectively. Finally, SVM is trained based on LBP feature. The recognition result of SVM and the result of skin-color ratio features are merged by weighted voting strategy, and then occluded faces are classified as three categories: concealed, partially concealed, and visible. Experimental results show that the proposed detection system can achieve desirable results in intelligent video surveillance systems.
- D. T. Lin and M. J. Liu, “Face occlusion detection for automated teller machine surveillance,” in Advances in Image and Video Technology. Springer, pp. 641–651, 2006.
- G. Kim, J. K. Suhr, H. G. Jung, and J. Kim, “Face occlusion detection by using b-spline active contour and skin color information,” in 11th International Conference on IEEE Control Automation Robotics & Vision (ICARCV), pp. 627–632, 2010.
- X. Zhang, L. Zhou ,T. Zhang, and J. Yang, “A novel efficient method for abnormal face detection in ATM,” in International Conference on. IEEE Audio, Language and Image Processing (ICALIP, pp. 695–700), 2014.
- T. Charoenpong, C. Nuthong, and U. Watchareeruetai, “A new method for occluded face detection from single viewpoint of head,” in 11th International Conference on IEEE Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTICON), pp. 1-5, 2014.
- S. M. Yoon and S. C. Kee, “Detection of Partially Occluded Face using Support Vector Machines,” in IAPR Workshop on Machine Vision Applications, pp. 546-549, 2002.
- R. Min, A. D’Angelo, and J. L. Dugelay, “Efficient scarf detection prior to face recognition,” in Proceedings of the 18th European Signal Processing Conference, pp.259-263, 2010.
- G. N. Priya, and R. S. D. W. Banu, “Detection of occluded face image using mean based weight matrix and support vector machine,” in Journal of Computer Science, Vol. 8, no. 7, pp.1184-1190, 2012.
- S. Bianco, G. Ciocca, G. C. Guarnera, A. Scaggiante, and R. Schettini, “Scoring recognizability of faces for security applications,” in Proc. SPIE 9024, Image Processing: Machine Vision Applications VII, 90240L, 2014.
- J. Kim, Y. Sung, S. M. Yoon, and B. G. Park, “A new video surveillance system employing occluded face detection,” in Lecture Notes in Computer Science, vol. 3533, pp. 65-68, 2005.
- J. K. Suhr, S. Eum, H. G. Jung, G. Li, G. Kim and J. Kim. “Recognizability assessment of facial images for automated teller machine applications.” in Pattern Recognition, vol. 45, pp. 1899-1914, 2012.
- S. Eum, J. K. Suhr, and J. Kim. “Face Recognizability Evaluation for ATM Applications with Exceptional Occlusion Handling,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp.82-89, 2011.
- C. B. Jin, S. Z. Li, T. D. Do, and H. Kim, “Real-Time Human Action Recognition Using CNN Over Temporal Images for Static Video Surveillance Cameras,” in Advances in Multimedia Information Processing, Vol. 9315, pp. 330-339, 2015.
- Y. Xia, and F. Coenen, “Face Occlusion Detection Based on Multi-task Convolution Neural Network,” in Proceedings of 12th International Conference on IEEE Fuzzy Systems and Knowledge Discovery (FSKD), pp.375-379, 2015.
- K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis, “Real-time foreground-background segmentation using codebook model,” in Real-Time Imaging, Vol. 11, no. 3 pp. 172–185, 2005.
- N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), vol. 1, pp. 886 –893, 2005.
- P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, “Object detection with discriminatively trained part-based models,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 9, pp. 1627–1645, 2010.
- M. Hirabayashi, S. Kato, M. Edahiro, K. Takeda, T. Kawano, and S. Mita, “GPU Implementations of Object Detection using HOG Features and Deformable Models,” in Proceedings of the IEEE International Conference on Cyber-Physical Systems, Networks, and Applications, pages 106-111, 2013.
- M. Szkudlarek and M. Pietruszka, “Fast GPU and CPU computing for Head Position Estimation,” in Proceedings of the Federated Conference on Computer Science and Information System, pp. 231-240, 2015. http://dx.doi.org/10.15439/2015F410.
- D. Lee, J. Wang, K.N. Plataniotis, “Contribution of skin color cue in face detection applications,” in: M.C. Emre, B. Smolka (Eds.), in Advances in Low-Level Color Image Processing, Springer, Netherlands, pp. 367–407, 2014.
- K. Nasrollahi and T. B. Moeslund, “Complete face logs for video sequences using quality face measures,” in IET International Journal of Signal Processing, vol.3, no. 4, pp. 289–300, 2009.
- T. Ojala and M. Pietikainen, “Unsupervised Texture Segmentation Using Feature Distributions,” in Pattern Recognition, vol. 32, pp. 477-486, 1999
- Z. Zhang, J. Iria, C. Brewster, and F. Ciravegna, “A comparative evaluation of term recognition algorithms,” in Proceedings of the sixth international conference of Language Resources and Evaluation (LREC), pp. 2108-2113, 2008.
- Z. Zhang, H. Gunes and M. Piccardi, “Head detection for video surveillance based on categorical hair and skin colour models,” in Proceedings of IEEE International Conference on Image Processing (ICIP), pp. 1137–1140, 2009.