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.

中文翻译：

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基于样本扩展的多分辨率字典学习方法及其在人脸识别中的应用

近年来，字典学习方法被广泛应用于人脸识别并取得了良好的性能。然而，大多数字典学习方法有两个问题。首先，他们只关注原始图像的分辨率，没有考虑不同分辨率对字典性能的影响。当得到的字典用于解决实际应用问题时，对可能与原始图像分辨率有较大差异的现实世界图像的识别结果会非常令人失望。其次，由于训练样本不足，词典的功能会下降。针对以上问题，本文提出了一种基于样本扩展的多分辨率字典学习方法。我们将原始图像转换为不同的分辨率，并为每个分辨率生成一个字典。同样，对于原始图像生成的合理虚拟图像的每个分辨率也生成字典。然后，对于一个测试样本，采用一种简单高效的分数融合方案，将原始图像和虚拟图像的分数相结合，得到最终的分类分数。我们在多个人脸数据库上进行了实验，结果表明我们的方法比一些最先进的方法具有更好的性能。采用一种简单高效的分数融合方案，将原始图像和虚拟图像的分数相结合，得到最终的分类分数。我们在多个人脸数据库上进行了实验，结果表明我们的方法比一些最先进的方法具有更好的性能。采用一种简单高效的分数融合方案，将原始图像和虚拟图像的分数相结合，得到最终的分类分数。我们在多个人脸数据库上进行了实验，结果表明我们的方法比一些最先进的方法具有更好的性能。