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A Social Tagging Recommendation Model Based on Improved Artificial Fish Swarm Algorithm and Tensor Decomposition Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Face Detection in a Complex Background Using Cascaded Conventional Networks

Authors : Jianjun Li, Juxian Wang, Chin-Chen Chang, Zhuo Tang, Zhenxing Luo

Published in: Security with Intelligent Computing and Big-data Services

Publisher: Springer International Publishing

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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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Metadata
Title
Face Detection in a Complex Background Using Cascaded Conventional Networks
Authors
Jianjun Li
Juxian Wang
Chin-Chen Chang
Zhuo Tang
Zhenxing Luo
Copyright Year
2018
Book
Security with Intelligent Computing and Big-data Services

Print ISBN: 978-3-319-76450-4

Electronic ISBN: 978-3-319-76451-1

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-76451-1_9

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