Abstract
Face recognition which is of few advantages such as natural and non-contact to realize fluent interaction and cooperation between human and robot, has been one of important and common issues in the fields of computer vision and biometrics identification. However, the achievement of face recognition also meet few issues such as disturbances or variations in facial expression, pose, shade and environmental illumination to solve. For this reason, an autonomous face identification system based on deep learning is proposed in this article, which should be divided into 4 stages. Firstly, RGB-D images including one or more faces are captured by Kinect v2. Secondly, an algorithm of multi-view faces detection has been proposed by introducing candidate regions after filters of local binary Haar-like feature into Multi-layer perceptron (MLP) in order to obtain every candidate face area. Thirdly, typical face feature points such as left eye, right eye, nose tip, left corner of the mouth and the right corner of the mouth are located and aligned by Stacked Auto-Encoder (SAE) accurately. Finally, VIPLFaceNet has been applied to identify the similarity and difference between the image to be determined and any template in the face image database. Experimental results have shown that the proposed system not only can detect multi-faces belonging to different persons, but also could achieve well identification results with the correctness of no less than 70% regardless of few disturbance of pose, expression and illumination.
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This work is supported by 2018 Practice Training Program of Beijing Information Science and Technology University.
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Liu, M., Dong, N., Tan, Q., Yan, B., Zhao, J. (2019). Research on Autonomous Face Recognition System for Spatial Human-Robotic Interaction Based on Deep Learning. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science(), vol 11744. Springer, Cham. https://doi.org/10.1007/978-3-030-27541-9_12
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