Abstract
Face detection in the wild is a challenging task within the field of computer vision. Many face detectors fail to distinguish face images and non-face images because intra-class variations surpass inter-class variations. To overcome it, we propose a metric learning based false positives filtering for face detection. With 8 average faces as standard face, we apply metric learning to seek a linear transformation to reduce the distance between face images and standard faces while enlarge the distance between non-face images and standard faces. To solve our defining objective function for metric learning, we adopt a batch-stochastic gradient descent scheme, with which we can get stable solution fast. The results on FDDB and our self-collected dataset show a good performance of our method for improving Viola-Jones face detectors.
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Zhang, N., Han, J., Hu, J., Deng, W. (2015). Metric Learning Based False Positives Filtering for Face Detection. 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_8
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DOI: https://doi.org/10.1007/978-3-319-25417-3_8
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