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http://dx.doi.org/10.9717/kmms.2016.19.8.1310

Fast and Robust Face Detection based on CNN in Wild Environment  

Song, Junam (School of Electrical Engineering, KAIST)
Kim, Hyung-Il (School of Electrical Engineering, KAIST)
Ro, Yong Man (School of Electrical Engineering, KAIST)
Publication Information
Journal of Korea Multimedia Society / v.19, no.8, 2016 , pp. 1310-1319 More about this Journal
Abstract
Face detection is the first step in a wide range of face applications. However, detecting faces in the wild is still a challenging task due to the wide range of variations in pose, scale, and occlusions. Recently, many deep learning methods have been proposed for face detection. However, further improvements are required in the wild. Another important issue to be considered in the face detection is the computational complexity. Current state-of-the-art deep learning methods require a large number of patches to deal with varying scales and the arbitrary image sizes, which result in an increased computational complexity. To reduce the complexity while achieving better detection accuracy, we propose a fully convolutional network-based face detection that can take arbitrarily-sized input and produce feature maps (heat maps) corresponding to the input image size. To deal with the various face scales, a multi-scale network architecture that utilizes the facial components when learning the feature maps is proposed. On top of it, we design multi-task learning technique to improve detection performance. Extensive experiments have been conducted on the FDDB dataset. The experimental results show that the proposed method outperforms state-of-the-art methods with the accuracy of 82.33% at 517 false alarms, while improving computational efficiency significantly.
Keywords
Face Detection; Deep Learning; Fully Convolutional Network; Heat Map; Facial Component; Face Bound Regression;
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Times Cited By KSCI : 2  (Citation Analysis)
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