|Deep learning for low-resolution human face image recognition
|Hong Kong Polytechnic University -- Dissertations
Human face recognition (Computer science)
Image analysis -- Data processing
Image processing -- Digital techniques
|Faculty of Engineering
|1, 1, 1, 47 pages : color illustrations
|For the reason that human face images captured by remote monitoring devices are usually of low quality and low resolutions, they are difficult to be accurately recognized in practice. Therefore, this dissertation proposes three different recognition methods for low-resolution human face images. First, we up-sample low-resolution face images and use a conventional eigenface algorithm to do recognition. Then, inspired by the cross-space idea, we adopt the coupled mapping method to project face images of different dimensions into a unified potential subspace, where the recognition can be realized. Finally, we propose a method of deep learning. We use the generated high-resolution images which come from the original low-resolution ones and their corresponding high-resolution images to train our convolutional neural network, and then extract features from the trained model, and the identification is further achieved by the nearest neighborhood method. We evaluate and compare the three proposed methods by using two widely acknowledged datasets. It is proved that the deep learning method has the best performance in low-resolution human face recognition under various conditions. The coupled mapping method outperforms the conventional method.
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