Abstract
In recent years, local classifiers have obtained great success in classification task due to its powerful discriminating ability on local regions. Based on it, we employ a locality-sensitive SVM (LSSVM) to build a local model on each local region to solve the problem of large intra-class variances between different face images. On the other hand, the use of SVM with local kernels was presented. Compared with the conventional global kernel, it’s more robust since it can utilize the local features which are influenced only specific parts under partial occlusion. So in order to detect face effectively, we want to utilize the global and local features of face comprehensively. Thus we combine the global and local kernels and apply the combination kernel to the LSSVM algorithm, proposing a robust face detection algorithm. Extensive experiments on the widely used CMU+MIT dataset and FDDB dataset demonstrate the robustness and validity of our algorithm.
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Chen, W.S., Dai, X.L., Pan, B.B.: A novel discriminant criterion based on feature fusion strategy for face recognition. Neurocomputing 159, 67–77 (2015)
Sun, Y., Wang, X.G., Tang, X.O.: Deep convolutional network cascade for facial point detection. In: CVPR, pp. 3476–3483. IEEE Press, Portland (2013)
Cheng, H., Tan, P.N., Jin, R.: Localized support vector machine and its efficient algorithm. In: SIAM Conference on Data Mining, pp. 461–466. SIAM Press, Minneapolis (2007)
Qi, G.J., Tian, Q., Huang, T.: Locality-sensitive support vector machine by exploring local correlation and global regularization. In: CVPR, pp. 841–848. IEEE Press, Providence (2011)
Hotta, K.: Local normalized linear summation kernel for fast and robust recognition. Pattern Recognition 43, 906–913 (2010)
Hotta, K.: Robust face recognition under partial occlusion based on support vector machine with local Gaussian summation kernel. Image and Vision Computing 26, 1490–1498 (2008)
Hotta, K.: View independent face detection based on horizontal rectangular features and accuracy improvement using combination kernel of various sizes. Pattern Recognition 42, 437–444 (2009)
Rowley, H.A., Baluja, S., Kanade, T.: Neural network-based face detection. PAMI 20, 23–38 (1998)
Viola, P., Jones, M.: Robust real-time face detection. International Journal of Computer Vision 57, 137–154 (2004)
Li, J., Wang, T., Zhang, Y.: Face detection using SURF cascade. In: ICCV Workshops, pp. 2183–2190. IEEE Press, Barcelona (2011)
Jun, B., Kim, D.: Robust face detection using local gradient patterns and evidence accumulation. Pattern Recognition 45, 3304–3316 (2012)
Zhou, S., Yin, J.: Face Detection using Multi-block Local Gradient Patterns and Support Vector Machine. Journal of Computational Information Systems 10, 1767–1776 (2014)
Chen, Y., Han, C.: A CNN-Based Face Detector with a Simple Feature Map and a Coarse-to-fine Classifier. PAMI 99, 1–13 (2009)
Jain, V., Learned-Miller, E.: FDDB: A benchmark for face detection in unconstrained settings. Technical Report UM-CS-2010-009, University of Massachusetts, Amherst (2010)
Venkatesh, B.S., Marcel, S.: Fast bounding box estimation based face detection. In: ECCV Workshops on Face Detection. Springer Press, Crete (2010)
Jain, V., Learned-Miller, E.: Online domain adaptation of a pre-trained cascade of classifiers. In: CVPR, pp. 577–584. IEEE Press, Providence (2011)
Segui, S., Drozdzal, M., Radeva, P., et al.: An integrated approach to contextual face detection. In: ICPPAM, pp. 90–97. Springer Press, Vilamoura (2012)
Markus, N., Frljak, M., Pandzic, I.: A method for object detection based on pixel intensity comparisons organized in decision trees, arXiv preprint arXiv:1305.4537 (2013)
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Li, X., Tao, Q., Zhao, J., Mao, Y., Zhan, S. (2015). Robust Face Detection Based on Enhanced Local Sensitive Support Vector Machine. In: Yang, J., Yang, J., Sun, Z., Shan, S., Zheng, W., Feng, J. (eds) Biometric Recognition. CCBR 2015. Lecture Notes in Computer Science(), vol 9428. Springer, Cham. https://doi.org/10.1007/978-3-319-25417-3_13
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DOI: https://doi.org/10.1007/978-3-319-25417-3_13
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