## 2 Related Work  
With the growth of importance in security, biometrics encoding personal characteristics such as face, iris and fingerprints are widely used for person identification. Among them, face recognition is becoming more prevalent thanks to the recent deep learning-based methods steadily improving the identification accuracy. Face recognition can be categorized into three groups: holistic-based, partial-based, and deep learning-based approaches. Holistic-based approaches utilize global facial characteristics as features for identification. Many traditional methods such as Eigenfaces [5] and Fisherfaces [6] (based on the linear discriminant analysis (LDA)), could be classified as holistic-based approaches. [PERSON] et al., introduced Eigenface [5], which is a face recognition method based on the principal component analysis (PCA). This method approximates an image as a vector of principal components by projecting into the subspace of eigenvectors, which are calculated from the covariance matrix of the training data. In this work, the eigenvectors represent the axes which can express the distribution of training data. By sorting and selecting eigenvectors according to their respective eigenvalues, one can reduce the dimension of input image without losing too much information. [PERSON] et al., proposed a Fisherface-based face recognition method, which has higher recognition accuracy than that of Eigenface [6]. The method is based on the LDA and projects given images into  
Figure 1: An overview of a generic CNN architecture. [PERSON]'s linear discriminant vectors, which best explain the inter-class and intra-class relationships. These two methods are the representative methods of the holistic-based approaches. Since holistic-based approaches compute a vectorized matrix from the training dataset, these methods are highly dependent to the characteristics of the training set used, and consequently they are sensitive to environmental conditions such as illumination, pose and expression. Therefore, holistic-based approaches require image normalization (e.g., through alignment of face region or normalization of illumination) in order to improve the matching accuracy. Partial-based approaches were proposed to overcome the aforementioned weaknesses of the holistic-based approaches. These approaches divide face region into grids and extract local facial features. Well-known partial-based methods include the local binary patterns (LBP), scale-invariant feature transform (SIFT) and histogram of gradients (HOG). [PERSON] et al., proposed a face recognition method based on local binary patterns (LBP), which analyze the patterns of the facial region [7]. This LBP method generates binary patterns by comparing the value of the center pixel with that of its neighboring pixels in each local window. Assuming that the size of the local window is 3 \(\times\) 3 (i.e., nine pixels), the LBP operator represents each pixel as 0 or 1 using a simple binary comparison. For instance, if a pixel value is greater than that of the center pixel, then LBP outputs 1, otherwise it outputs 0. Also, the LBP-based face recognition method transforms the extracted binary codes into uniform patterns. The uniform patterns consider the number of transitions from 0 to 1 and the length of 1. From that, it can describe the texture of facial skins which include edge and point information. Finally it represents these patterns as a histogram and recognizes user by calculating the distance between the enrolled features and the features from input image. The LBP-based method demonstrated then-high performance despite its relatively simple architecture. Direct extensions of this work include local ternary patterns (LTP) and three-patch LBP (TP-LBP) [8; 9]. After the success of the LBP method, several partial-based approaches were proposed and utilized. In [10; 11], the authors applied the SIFT and HOG algorithms respectively for partial feature-based face recognition.