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Detecting eyes in images is fundamental for many computer vision applications including face detection, face recognition, and human-computer interaction. Most existing methods are designed and tested on datasets acquired under controlled lab settings (e.g., fixed scale, known poses, clean background, etc.), leaving their performance to be further examined on real-world, uncontrolled images, such as...
In this paper, a part-based technique for real time detection of users' faces on mobile devices is proposed. This method is specifically designed for detecting partially cropped and occluded faces captured using a smartphone's front-facing camera for continuous authentication. The key idea is to detect facial segments in the frame and cluster the results to obtain the region which is most likely to...
In this paper, a novel progressive strategy is proposed to teach the machine to accomplish face detection in the wild. Firstly, deep model named Fully-connected Face Classifier (FCFC) is built up. With the targeted training data, FCFC learns the knowledge corresponding to distinguish face in various pose, facial expression, occlusion proportion, and blur degree from background gradually. Secondly,...
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