## 2 Related Work
Both methods have the advantage of being able to robustly extract facial features in presence of local pose variations and illumination changes. [PERSON] et al. introduced Volume-SIFT (VSIFT) and Partial-Descriptor-SIFT (PDSIFT), which are extended versions of the original SIFT descriptor [10]. The SIFT algorithm is commonly used for object detection and recognition as its descriptor is invariant to changes in scale and rotation. By applying SIFT to face recognition tasks, the authors were able to extract robust facial features and compare the matching performance against other methods based on holistic approaches. [PERSON] et al., applied HOG features for face recognition [11]; the authors detected facial landmarks from input image, around which they set small areas of patches and extracted a HOG descriptor from each patch. In this work, HOG features were extracted by applying patches of different sizes, and combining these varying features were shown to improve the recognition performance. [PERSON] et al. argued that high dimensionality leads to high performance, and demonstrated this by comparing the proposed high-dimensional LBP method against the original method and other then-state-of-the-art methods [12]. In this work, the authors initially detected 27 facial landmarks from input image and created one 40 \(\times\) 40-sized patch per landmark centered on the corresponding landmark position. The designated patch region was divided into 4 \(\times\) 4 non-overlapping cells, and facial features were extracted from each divided cell by applying the LBP operator. In order to enhance scale invariance, the authors resized the input image over five steps and performed the above procedure repeatedly. From this, high-dimensional LBP features were generated by concatenating the extracted feature vector from each cell, leading to a feature space dimension of around 100 K. The authors applied PCA to reduce this dimension to a controllable size of 400. Experimental results showed that the high-dimensional LBP features outperformed the low-dimensional LBP and other state-of-the-art methods for the Labeled Faces in the Wild (LFW) dataset with unrestricted protocol [13]. Its matching accuracy is 93.18%, which was the-state-of-the art accuracy. (The matching accuracy of the prior best was about 90%). Since the success of deep learning in many areas of computer vision, several deep learning-based methods have been proposed for face recognition. Overall, these methods attempt to extract feature vectors encompassing both holistic and partial characteristics. [PERSON] et al. proposed a deep learning framework for face recognition called \"DeepFace\", which aligns input face image based on 3D face model and extracts the corresponding feature vector from a nine-layer CNN [1]. The model was trained by using approximately four million facial images acquired from around 4000 people. DeepFace adopts an end-to-end metric learning model called the Siamese network, which directly predicts whether the same person is observed in both of two input images. To enhance the recognition performance, the authors combined several deep learning-based method, including the previously-mentioned Siamese network. Subsequently, it achieved 97.37% of matching accuracy on the LFW dataset with unrestricted protocol, which is very close to human recognition accuracy (\(\approx\) 97.5%). After the introduction of DeepFace, many other deep learning-based methods have been proposed. [PERSON] et al. 's deep hidden identity features (DeepID) [2] detects five facial landmarks by using [PERSON] et al. 's method [14] and aligns input face by considering the centers of eyes and mouth. From the aligned face image, the authors extracted feature vectors from 60 face patches, which are generated by considering 10 regions, three scales, and RGB or gray channels. They trained 60 convolutional networks (one per face patch) where each network outputs two 160-dimensional feature vectors.