Deep Heterogeneous Feature Fusion for Template-Based Face Recognition

15 Feb 2017Navaneeth BodlaJingxiao ZhengHongyu XuJun-Cheng ChenCarlos CastilloRama Chellappa

Although deep learning has yielded impressive performance for face recognition, many studies have shown that different networks learn different feature maps: while some networks are more receptive to pose and illumination others appear to capture more local information. Thus, in this work, we propose a deep heterogeneous feature fusion network to exploit the complementary information present in features generated by different deep convolutional neural networks (DCNNs) for template-based face recognition, where a template refers to a set of still face images or video frames from different sources which introduces more blur, pose, illumination and other variations than traditional face datasets... (read more)

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