# UFaceNet: Research on Multi-Task Face Recognition Algorithm Based on CNN
## 2 Related Work
It believes that the deeper network structure can better extract the global features and improve the problem of local optimality caused by the initial inaccuracy, but the network does not detect well when the face is occluded. [PERSON] et al. [28] made improvements on DCNN, proposing a four-level cascade network from coarse to fine facial point detection. It uses the image of the face area predicted by CNN as the input of the network, which improves the positioning accuracy of the latter stages. Similar to DCNN, this method also has the problem of complex network structure. [PERSON] et al. [29] proposed the MTCNN (Multi-task Cascaded Convolutional Network), which can perform face detection and facial point detection simultaneously, making full use of the potential links between the two tasks. MTCNN has a certain improvement in speed and accuracy, but the network structure is complex and only detects five keypoints on the face. DAN, Deep Alignment Network [30], is also a method based on cascaded neural networks. It introduces a keypoint heat map, and each level of the network uses the entire image as input. Model positioning is accurate, but the calculation speed still needs to be improved. The main three algorithms of facial point detection are: (1) Model-based ASM and AAM models are simple, the architecture is clear and easy to understand and apply, but its exhaustive iterative search limits the performance of the method. (2) The calculation speed based on the cascading shape regression model is fast, but such models have problems that the parameters are difficult to adjust. (3) The method based on deep learning has a strong feature extraction ability of the convolutional neural network, and the detection is more accurate, but the network is more complicated, and the cascade structure limits the performance of the model. The purpose of face recognition is to extract feature information from the face image and identify the identity based on the feature. The general face recognition process is divided into two steps. The first step is facial feature extraction and feature selection, and the second step is object classification. Popular traditional recognition algorithms include principal component analysis using feature faces, linear discriminant analysis, the Fisherface algorithm, the hidden Markov model, etc. The method of identifying feature faces is developed by [PERSON] and [PERSON] and used by [PERSON] and [PERSON] for face classification [31]. The method takes the pixel points of the image as the original dimension unit and attempts to transform to another target space through one transformation, in which each face can be best distinguished. However, the performance of this method will decrease when the face is occluded. At the same time, the image analysis speed still needs to be optimized. The paper [32] first applies the hidden Markov model to the face recognition algorithm. The method trains the HMM on the spatial sequence of multiple sample images and obtains the two-dimensional hidden Markov model based on the top-down and left-to-right structural features of the face according to the natural characteristics that the facial features are fixed. The algorithm of the Cove model uses DCT as the observation vector to obtain a good recognition effect, but its disadvantage is that the structure is complicated and the calculation amount is large. In the past decade, Convolutional Neural Network (CNN) [33] has become one of the most popular techniques for solving computer vision problems. Many visual tasks, such as image classification, object detection, and face recognition, benefit from CNN's powerful learning and discriminative characterization. The CNN-based face recognition method usually regards CNN as a powerful feature extractor. DeepFace [34] uses CNN as a feature extractor for the face to train on 4 million facial images, and obtains 67 base points to transform the triangulated face into a 3D model to depth information, and then turns the face back.