The machine learning of small sample size is one of the most challenging problems in face recognition. Multiscale patch cooperative representation for face recognition provides multiple patch scales to a sample set. In each patch scale, all samples in the training set and test set are segmented into patches of the same size, followed by a collaborative representation classification. A sample belongs to the category in which the number of patch blocks is the highest. Under a single scale, however, the patch blocks of a sample often belong to different categories. If the decision value of the sample is defined as the category with the largest number of patches, the possibility of the sample belonging to other categories is often disregarded; this will then significantly reduce the recognition accuracy. Therefore, a multiscale patched collaborative representation based on fuzzy decision is proposed herein. In a single scale, among all the patch blocks in a sample, the proportion of patch blocks belonging to a category to the total patch blocks is used to represent the degree of a sample belonging to the category. Hence, a fuzzy decision matrix can be obtained for a sample set in each scale. The elements of the decision matrix represent the possibility of samples belonging to categories, thereby solving the absolute problem of classification. Corresponding weights are applied to the patch scales, and multiscale outputs can be integrated by regularizing boundary distribution optimization. It is experimentally demonstrated that the proposed method exhibits high recognition accuracy and is superior to some existing algorithms.

小样本量的机器学习是人脸识别中最具挑战性的问题之一。用于面部识别的多尺度补丁协作表示为样本集提供了多个补丁尺度。在每个补丁规模中，训练集和测试集中的所有样本都被分割成相同大小的补丁，然后进行协作表示分类。样本属于其中补丁块数量最高的类别。但是，在单一尺度下，样本的色块通常属于不同的类别。如果将样本的决策值定义为补丁数量最多的类别，则通常会忽略样本属于其他类别的可能性；这样会大大降低识别精度。所以，本文提出了一种基于模糊决策的多尺度修补协作表示。在单个尺度上，样本中所有补丁块中，属于某个类别的补丁块占总补丁块的比例用于表示属于该类别的样本的程度。因此，可以获得针对每个尺度的样本集的模糊决策矩阵。决策矩阵的元素表示样本属于类别的可能性，从而解决了分类的绝对问题。将相应的权重应用于面片比例尺，并且可以通过规范化边界分布优化来集成多面尺输出。实验证明，该方法具有较高的识别精度，优于现有算法。