Face detection in a complex background using cascaded conventional networks

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Abstract

Although significant achievements have been achieved in the field of face detection recently, face detection under complex background is still a challenge issue. Especially, face detection has wide applications in real life, such as face recognition attendance system and crowd size estimation. In this paper, we propose a novel cascaded framework to tackle the challenges based on: blur, illumination, pose, expression and occlusion. Our framework adopt the localization of facial landmarks to boost up their performance. In addition, our detector extracts features from different layers of a deep residual network for complementary information of low-dimensional and high-dimensional features. Our method achieves notable results over the state-of-the-art techniques on the challenging WIDER FACE benchmark for face detection and our results show that average precision of 89.2%. Importantly, we demonstrate superior performance and robustness in a challenging environment.

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Li, J., Wang, J., Chang, C. C., Tang, Z., & Luo, Z. (2018). Face detection in a complex background using cascaded conventional networks. In Advances in Intelligent Systems and Computing (Vol. 733, pp. 86–97). Springer Verlag. https://doi.org/10.1007/978-3-319-76451-1_9

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