Yang Hua, Gong Chenting, Huang Kaiji, Song Kaiyou, Yin Zhouping
IEEE Trans Image Process. 2021;30:3858-3871. doi: 10.1109/TIP.2021.3065843. Epub 2021 Mar 25.
Most face recognition methods employ single-bit binary descriptors for face representation. The information from these methods is lost in the process of quantization from real-valued descriptors to binary descriptors, which greatly limits their robustness for face recognition. In this study, we propose a novel weighted feature histogram (WFH) method of multi-scale local patches using multi-bit binary descriptors for face recognition. First, to obtain multi-scale information of the face image, the local patches are extracted using a multi-scale local patch generation (MSLPG) method. Second, with the goal of reducing the quantization information loss of binary descriptors, a novel multi-bit local binary descriptor learning (MBLBDL) method is proposed to extract multi-bit local binary descriptors (MBLBDs). In MBLBDL, a learned mapping matrix and novel multi-bit coding rules are employed to project pixel difference vectors (PDVs) into the MBLBDs in each local patch. Finally, a novel robust weight learning (RWL) method is proposed to learn a set of robust weights for each patch to integrate the MBLBDs into the final face representation. In RWL, a codebook is first constructed by clustering MBLBDs on each local patch to extract a feature histogram. Then, considering that different parts of the face have different degrees of robustness to local changes, a set of weights is learned to concatenate the feature histograms of all local patches into the final representation of a face image. In addition, to further improve the performance for heterogeneous face recognition, a coupled WFH (C-WFH) method is proposed. C-WFH maintains the similarity of the corresponding MBLBDs and feature histograms for a pair of heterogeneous face images by means of a novel coupled feature learning (CFL) method to reduce the modality gap. A series of experiments are conducted on widely used face datasets to analyze the performance of WFH and C-WFH. Extensive experimental results show that WFH and C-WFH outperform state-of-the-art face recognition methods.
大多数人脸识别方法采用单比特二进制描述符来表示人脸。这些方法中的信息在从实值描述符量化为二进制描述符的过程中丢失，这极大地限制了它们在人脸识别中的鲁棒性。在本研究中，我们提出了一种新颖的加权特征直方图（WFH）方法，该方法使用多比特二进制描述符对多尺度局部补丁进行人脸识别。首先，为了获得人脸图像的多尺度信息，使用多尺度局部补丁生成（MSLPG）方法提取局部补丁。其次，为了减少二进制描述符的量化信息损失，提出了一种新颖的多比特局部二进制描述符学习（MBLBDL）方法来提取多比特局部二进制描述符（MBLBD）。在MBLBDL中，使用学习到的映射矩阵和新颖的多比特编码规则将像素差异向量（PDV）投影到每个局部补丁中的MBLBD中。最后，提出了一种新颖的鲁棒权重学习（RWL）方法，为每个补丁学习一组鲁棒权重，以将MBLBD集成到最终的人脸表示中。在RWL中，首先通过对每个局部补丁上的MBLBD进行聚类来构建码本，以提取特征直方图。然后，考虑到人脸的不同部分对局部变化具有不同程度的鲁棒性，学习一组权重，将所有局部补丁的特征直方图连接成人脸图像的最终表示。此外，为了进一步提高异质人脸识别的性能，提出了一种耦合WFH（C-WFH）方法。C-WFH通过一种新颖的耦合特征学习（CFL）方法来保持一对异质人脸图像的相应MBLBD和特征直方图的相似性，以减少模态差距。在广泛使用的人脸数据集上进行了一系列实验，以分析WFH和C-WFH的性能。大量实验结果表明，WFH和C-WFH优于现有最先进的人脸识别方法。
