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This paper addresses the problem of transferring CNNs pre-trained for face recognition to a face attribute prediction task. To transfer an off-the-shelf CNN to a novel task, a typical solution is to fine-tune the network towards the novel task. As demonstrated in the state-of-the-art face attribute prediction approach, fine-tuning the high-level CNN hidden layer by using labeled attribute data leads...
Predicting facial attributes from faces in the wild is very challenging due to pose and lighting variations in the real world. The key to this problem is to build proper feature representations to cope with these unfavourable conditions. Given the success of Convolutional Neural Network (CNN) in image classification, the high-level CNN feature, as an intuitive and reasonable choice, has been widely...
Predicting attributes from face images in the wild is a challenging computer vision problem. To automatically describe face attributes from face containing images, traditionally one needs to cascade three technical blocks — face localization, facial descriptor construction, and attribute classification — in a pipeline. As a typical classification problem, face attribute prediction has been addressed...
Great progress in face recognition technology has been made recently. Such advances will provide us the possibility to build a new generation of search engine: Face Google, searching from person photos. It is very challenging to find a person from a very large or extremely large database which might hold face images of millions or hundred millions of people. The indexing technology used in most commercial...
Automatic human lip tracking is one of the key components tomany facial image analysis tasks, such as, lip-reading and emotion from lips. It has been a classical hard image analysis problem over decades. In this paper, we propose an indirect lip tracking strategy: `lipless tracking'. It is based on the observation that many of us don't have clear lips and some even don't have visible lips. The strategy...
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