Yafeng Deng

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How can a single fully convolutional neural network (FCN) perform on object detection? We introduce DenseBox, a unified end-to-end FCN framework that directly predicts bounding boxes and object class confidences through all locations and scales of an image. Our contribution is twofold. First, we show that a single FCN, if designed and optimized carefully,(More)
—Face Recognition has been studied for many decades. As opposed to traditional hand-crafted features such as LBP and HOG, much more sophisticated features can be learned automatically by deep learning methods in a data-driven way. In this paper, we propose a two-stage approach that combines a multi-patch deep CNN and deep metric learning, which extracts low(More)
Viola et al. have introduced a rapid object detection framework based on a boosted cascade of simple feature classifiers. In this paper we extend their work and achieve two contributions. Firstly, we propose a novel feature definition and introduce a feature shape mask to represent it. The defined features are scale-invariant which means the features can be(More)
In this paper, we propose a fast and robust face detection method. We train a cascade-structured classifier with boosted haar-like features which uses intensity information only. To speed up the process, we integrate motion energy into the cascade-structured classifier. Motion energy can represent moving the extent of the candidate regions, which is used to(More)
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