Multi-resolution dictionary learning method based on sample expansion and its application in face recognition

In recent years, dictionary learning method has been widely applied to face recognition and achieved good performance. However, most dictionary learning methods have two problems. First, they focused on only the resolution of the original images and did not consider impact of different resolutions on dictionary performance. When the attained dictionary is used to solve practical application problems, the recognition result of real-world images that may have a large difference in resolution with the original images will be very disappointed. Second, function of the dictionary will decrease due to insufficient training samples. Considering the above problems, this paper proposes a multi-resolution dictionary learning method based on sample expansion. We convert the original images to different resolutions and generate a dictionary for each resolution. Similarly, a dictionary is also produced for each resolution of reasonable virtual images generated by the original images. Then, for a test sample, a simple and efficient score fusion scheme is used to combine scores of the original image and the virtual image to obtain the ultimate classification score. We have performed experiments on multiple face databases, and the results show that our method has better performance than some state-of-the-art methods.