# UFaceNet: Research on Multi-Task Face Recognition Algorithm Based on CNN
## 2 Related Work
CNN-LSTM [18] introduces deep learning to liveness detection earlier, and simulates traditional liveness detection through CNN, but the effect is not good. [PERSON] et al. [19] designed a depth framework to replace the two-category problem with a targeted feature monitoring problem. [PERSON] et al. [20] used VGG16 as the basic network to directly add liveness detection to the face detection network. The detected bounding box includes three categories of confidence: background, living face, and nonliving face. Liveness detection can be divided into traditional methods based on image quality, texture information, and deep learning-based methods. The former is not effective in some special cases, while the latter is limited by the small number of data samples, which makes the network training difficult to fit, and the performance struggles to surpass the traditional method. Facial point detection refers to the image of a given face, finding the position and contour information of the key areas of the face. Facial point detection is roughly divided into three types: model-based, ASM and AAM methods; cascading shape regression; and deep learning-based methods. Active Shape Model (ASM) [21] first aligns the training images so that the images are rotated, scaled, and translated as close as possible to a selected reference image, and local features are constructed for each keypoint. When searching for shapes, ASM first calculates the position of the eye, aligns the faces with a simple scale and rotation change, and then searches for the vicinity of the aligned points to match the local keypoint, obtains the preliminary shape, and then corrects it using the average face model. Active Appearance Models (AAM) [22] make improvements to ASM, which not only uses shape constraints but also adds texture features throughout the face area. Such linear models struggle to obtain better results under occlusion, special expressions, poses, and illumination changes, and their methods of searching for keypoints to exhaustive iterations similarly limit the computational efficiency of the method. Cascaded waveform regression (CPR) [23] specifies the initial prediction values and gradually refines them through a series of linear models. Each regression relies on the output of the previous regression to perform simple image operations. The entire system can automatically learn from the training samples. This algorithm is similar to random forest regression. It is a clear and simple regression algorithm. It can train a good model with a small amount of training data, but the model only detects three keypoints of the face. [PERSON] et al. [24] inherited the idea of CPR using simple features and cascading tree structure to complete the classification and regression. It can do face detection and facial point detection at the same time. The calculation speed is fast, and the memory is small, but the model parameters are too numerous, and difficult to adjust. Local Binary Feature (LBF) [25] is a tree-based method that learns the local binary features of each keypoint and then uses linear regression to detect keypoints by combining the features. The algorithm can be divided into three processes: feature extraction, LBF coding, and acquisition of shape increments. The model is fast and accurate, but the model only detects five keypoints on the face. Ensemble of Regression Tress (EERT) [26] uses the GBDT algorithm to build a cascaded residual tree, and then gradually returns to the key point in the iteration. The model occupies less memory, and the calculation is fast, but the model is larger. [PERSON] et al. [27] first applied CNN to facial point detection and proposed a cascaded CNN network DCNN (Deep Convolutional Network). This method belongs to the cascade regression method. It detects five facial keypoints by designing a three-layer convolutional neural network. It focuses on the depth of the first-level network.