# Multi-Descriptor Random Sampling for Patch-Based Face Recognition
## 1 Introduction  
Face images can be captured easily at a distance and can also be used in various applications including surveillance, tracking, access control, etc. Therefore, face modality has been widely investigated in the biometric research field compared to other biometric modalities such as iris, fingerprint, and palmprint counterparts. Currently, the human face can be accurately recognised in a restricted environment. However, in an unrestricted environment, several challenges are encountered where faces are exposed to distortions. These distortions include illumination changes, pose variations, and partial occlusion. Moreover, while multiple algorithms have been proposed to tackle them in recent years, they have their limitations or requirements that cannot be met for faces in the wild. An image-based recognition system comprises of a feature extraction and representation process followed by a classification stage. Feature extraction methods can be classified into two main approaches: holistic feature-based and local feature-based methods [1]. In holistic approaches, the features extracted from the whole images are processed using either global linear, nonlinear statistical techniques or combined. The more conventional holistic methods include the popular linear techniques such as Principal Component analysis (PCA) method [2], Independent Component Analysis (ICA) [3], and Linear Discriminant Analysis (LDA) [4]. However, these methods may not be efficient due to the nonlinear characteristics of the face images. Therefore, some nonlinear kernel-based techniques have been investigated to address the problem by exploiting the contours of face images including the information details of the curves. Kernel Principal component analysis (KPCA) and Kernel Fisher Analysis (KFA) [5; 6] are widely used methods of this category. Local feature-based approaches are proven to be more robust to deal with complex backgrounds and occlusions inherently present in real image data. Unlike global descriptors which compute features from the whole image, local descriptors [7] have been shown to be more effective. Patch-based face recognition, which was proposed in [8], is another effective technique and operates by dividing an image into multiple overlapping or non-overlapping patches using either global or local descriptors for matching. In the case of patch-based approaches, the extraction of the local features is performed for each region (or patch) of the images where each face image is divided into a number of either overlapping or non-overlapping blocks. There exists a number of approaches for patch-based face recognition in the literature. The authors in [9] have proposed a feature concatenation method including a block selection with similarity measure. On the other hand, the work described in [10] suggests the use of a weight for classification results of the patches by calculating the genuine classification rates extracted from the test set. The work [11] proposes to employ the concept of subspace by using a majority voting scheme for combining the results of classification generated from the patches using random subspaces. The work discussed in [12] proposes carrying out the training of classifiers using separate random patches of the images and suggests a combination using a two-step layer decision: (i) using a weighted summation and (ii) combining the outcome from local ensemble classifiers with that of a global classifier obtained from the whole faces. The work described in [13] proposes to determine and select face areas containing more discriminative information for use in the classification phase. Although this proposed method shows high effectiveness while being highly robust against the issues of illumination distortions and partial occlusions, the classification performances are not significant, which is mainly due to the fact that one single classifier is constructed for all the image patches. The authors in [14] propose to first determine the area having the largest matching score at each point of the face.