# Deep Learning and Face Recognition: Face Recognition Approach Based on the DS-CDCN Algorithm
## 2 Related Works  
DL neural network is a machine learning model that utilizes multiple layers of neurons for learning and inference, enabling adaptive responses to complex tasks by learning features and patterns from large amounts of data. To address the problems of neural network cell vanishing and exploding gradients, [PERSON]'s research group used generalized re-fuzzy neurons to optimize the learning process and combined them with a backpropagation algorithm for network training. It was shown that this method improves the optimization rate of the model [6]. [PERSON] et al. proposed an anomaly detection model with a multi-scale autoencoder and a deep feature extractor for DL industrial anomaly detection. Application data showed that the model could obtain a high area under the curve [7]. [PERSON]'s group addressed the adversarial training problem using an integration-based approach for autopilot control systems and proposed a black-box attack scheme based on boosting. The results indicated that the effectiveness of the method could reach 90% [8]. [PERSON] and his group members proposed a time series framework for predicting nonlinear systems, including generative performance-based models and long and short-term memory models, etc., and combined it with the K-means method for preprocessing and hypothesis validation. Experiments showed that the average standard error of the method was 0.23% [9]. [PERSON] et al. proposed a deep convolutional neural network (CNN) model. The model utilized algorithms such as stochastic gradient descent, Nesteroff-accelerated gradient, and adaptive gradient to compute the initial weights of the network and backpropagation learning was accelerated by model parameter updating. The results demonstrated that the deep CNN model has an accuracy of 99.05% [10].  
FR is a biometric method based on DL technology to achieve accurate identification and protection of personal identity by recognizing the features on the human face. In their discriminative deep multitask learning facial expression recognition system, [PERSON] et al. took into account the samples' local spatial distribution information as well as their class label information. The results showed that the method outperforms the traditional DL method [11]. Proposed a useful image pre-selection framework for facial emotion classification based on reinforcement learning [12], which contains two modules: an image selector and a coarse emotion classifier. The experimental results on the FER2013 dataset indicated that the effectiveness of this method was improved on the basis of traditional emotion classification methods. [PERSON] and his group members used various heuristic algorithms to optimize the feature extraction process for face expression detection, and the optimized features were fed into the residual network for representation and classification. The results showed that the quantum-inspired firefly algorithm performed better [13]. In order to extract deep features from the residual neural network and to enrich the feature information, [PERSON] et al. suggested an improved residual neural network-based face expression recognition algorithm. According to the experimental results, this method's recognition accuracy was 96.37% and 93.38% in two datasets, respectively [14]. [PERSON] et al. used a dual augmented capsule neural network for facial expression recognition, which utilizes a capsule neural network with multiple convolutional layers to enhance the feature representation. The results indicated that the method outperformed previous state-of-the-art methods [15].  
In summary, numerous scholars have conducted research and design for DL and FR, but the robustness and generalizability of these models are not adequately assessed. Additionally, some studies have used small datasets, which may limit the reliability and generalizability of the results. Therefore, this study proposes an FR method based on the DS-CDCN algorithm, which is expected to enhance the model's performance and the effectiveness of FR.