IMS-SSH: multiscale face detection method in unconstrained settings

Ke Zhang; Xinyao Guo; Yingxuan He; Xinsheng Wang; Yurong Guo; Qiaolin Ding

doi:10.1117/1.JEI.28.1.013035

15 February 2019 IMS-SSH: multiscale face detection method in unconstrained settings

Ke Zhang, Xinyao Guo, Yingxuan He, Xinsheng Wang, Yurong Guo, Qiaolin Ding

Author Affiliations +

Journal of Electronic Imaging, Vol. 28, Issue 1, 013035 (February 2019). https://doi.org/10.1117/1.JEI.28.1.013035

Abstract

Face detection from images has been an essential task in the field of computer vision, which is the premise of face recognition. Concerning the problem of low-face detection accuracy for multiscale face images in unconstrained settings, we propose single-stage headless face detector with IRNN, multilayer and soft-NMS face detection method based on single-stage headless face detector in unconstrained settings. First, considering the importance of the context for small-scale face detection, recurrent neural network initialized by the unit matrix module is joined to fully learn the contextual information. Second, in order to further improve the accuracy for multiscale face detection, multilayer fusion strategy is proposed, which learns the facial texture features from the lower layer in more detail. Finally, aiming at the face occlusion problem in unconstrained settings, soft nonmaximum suppression is used to join the predicted boxes together from different scales together to form final detection results. The results of experiments show that IMS-SSH significantly improves the performance of multiscale face detection in unconstrained settings, especially for small-scale face detection, and state-of-the-art performance is achieved on the unconstrained WIDER Face dataset.

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Ke Zhang, Xinyao Guo, Yingxuan He, Xinsheng Wang, Yurong Guo, and Qiaolin Ding "IMS-SSH: multiscale face detection method in unconstrained settings," Journal of Electronic Imaging 28(1), 013035 (15 February 2019). https://doi.org/10.1117/1.JEI.28.1.013035

Received: 25 October 2018; Accepted: 28 January 2019; Published: 15 February 2019

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KEYWORDS

Facial recognition systems

Sensors

Data modeling

Convolution

Network architectures

Computer vision technology

Environmental sensing

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