智能技术学报

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稿件标题: Face recognition using both visible light image and near-infrared image and a deep network
稿件作者: Kai Guo, Shuai Wu, Yong Xu
关键字词: Deep network; Face recognition; Illumination change; Insufficient training data
文章摘要: In recent years, deep networks has achieved outstanding performance in computer vision, especially in the field of face recognition. In terms of the performance for a face recognition model based on deep network, there are two main closely related factors: 1) the structure of the deep neural network, and 2) the number and quality of training data. In real applications, illumination change is one of the most important factors that significantly affect the performance of face recognition algorithms. As for deep network models, only if there is sufficient training data that has various illumination intensity could they achieve expected performance. However, such kind of training data is hard to collect in the real world. In this paper, focusing on the illumination change challenge, we propose a deep network model which takes both visible light image and near-infrared image into account to perform face recognition. Near-infrared image, as we know, is much less sensitive to illuminations. V
收录刊物: 2017年2卷1期
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