Due to the semantic gap between the insufficient facial features and facial identifying information, the single sample per person (SSPP) problem has always been a significant challenge in the field of facial recognition. To address this problem, this paper proposes a Self-Organizing Map (SOM)-based binary coding (SOM-BC) method, which extracts the middle-level semantic features by merging the SOM network with the Bag-of-Features (BoF) model. First, we extract the local features of the facial images using the SIFT descriptor. Next, inspired by human visual perception, we utilize a SOM neural network to obtain a visual words dictionary capable of reflecting the intrinsic structure of facial features in semantic space. Subsequently, a binary coding method is further proposed to map the local features into semantic space. Finally, we propose a simple but effective similarity measure method for classification. Experimental results on three public databases not only demonstrate the effectiveness of the proposed method, but also its high computational efficiency.

由于不足的面部特征和面部识别信息之间的语义鸿沟，每人单个样本（SSPP）问题一直是面部识别领域的重大挑战。为了解决这个问题，本文提出了一种基于自组织映射（SOM）的二进制编码（SOM-BC）方法，该方法通过将SOM网络与特征包（BoF）合并来提取中间层语义特征。模型。首先，我们使用SIFT描述符提取面部图像的局部特征。接下来，受人类视觉感知的启发，我们利用SOM神经网络获得了能够反映语义空间中面部特征的内在结构的视觉单词词典。随后，进一步提出了一种二进制编码方法来将局部特征映射到语义空间中。最后，我们提出了一种简单但有效的相似度度量方法进行分类。在三个公共数据库上的实验结果不仅证明了该方法的有效性，而且还具有很高的计算效率。