Title: Multi-task Cascaded and Densely Connected Convolutional Networks Applied to Human Face Detection and Facial Expression Recognition System
Authors: Chou, Kuan-Yu
Cheng, Yi-Wen
Chen, Wei-Ren
Chen, Yon-Ping
電控工程研究所
Institute of Electrical and Control Engineering
Keywords: Facial Expression Recognition(FER);Multi-task Cascaded Convolutional Networks(MTCNN);Densely Connected Convolutional Networks(DenseNet)
Issue Date: 1-Jan-2019
Abstract: Face detection and recognition is an important issue and a difficult task in computer vision and human-computer interaction. Recently, with the development of deep learning, several related technologies have been proposed for face detection and facial expression recognition (FER), and the outstanding convolutional neural networks are the most common used in this field. This thesis applies the multi-task cascade convolutional neural network to face detection, and then designs the real-time FER system based on densely connected convolution network (DenseNet). The system first scales the input image to an image pyramid, and then uses the hierarchical network to determine whether a candidate window includes a human face. If a face exists, then send the candidate window to the FER system. Since DenseNet possesses the property of feature reuse, it can effectively reduce the amount of parameters and computation efforts, beneficial to develop the real-time system. In order to capture the variation of facial muscle in different expressions, this architecture adopts convolution operations with a stride 1 and tries different numbers of dense blocks. Through experiments, the proposed system can achieve real-time recognition in 30FPS and with recognition accuracy better than human eyes.
URI: http://hdl.handle.net/11536/155515
ISBN: 978-1-7281-3846-6
ISSN: 2473-7240
Journal: 2019 INTERNATIONAL AUTOMATIC CONTROL CONFERENCE (CACS)
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Appears in Collections:Conferences Paper