From Event: SPIE Commercial + Scientific Sensing and Imaging, 2017
Autonomous facial recognition system is widely used in real-life applications, such as homeland border security, law enforcement identification and authentication, and video-based surveillance analysis. Issues like low image quality, non-uniform illumination as well as variations in poses and facial expressions can impair the performance of recognition systems. To address the non-uniform illumination challenge, we present a novel robust autonomous facial recognition system inspired by the human visual system based, so called, logarithmical image visualization technique. In this paper, the proposed method, for the first time, utilizes the logarithmical image visualization technique coupled with the local binary pattern to perform discriminative feature extraction for facial recognition system. The Yale database, the Yale-B database and the ATT database are used for computer simulation accuracy and efficiency testing. The extensive computer simulation demonstrates the method’s efficiency, accuracy, and robustness of illumination invariance for facial recognition.
Qianwen Wan, Karen Panetta, and Sos Agaian, "Autonomous facial recognition system inspired by human visual system based logarithmical image visualization technique," Proc. SPIE 10221, Mobile Multimedia/Image Processing, Security, and Applications 2017, 1022106 (Presented at SPIE Commercial + Scientific Sensing and Imaging: April 10, 2017; Published: 10 May 2017); https://doi.org/10.1117/12.2263598.
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