The Research On Face Recognition Based On Deep Learning Algorithm

With the rapid development of computer science,human-computer interaction has been integrated into people's work and life,artificial intelligence technology has become one of the most active and important research topics in the field of computer.Face recognition technology,it is one of the core technology in the field of artificial intelligence.After nearly three decades of rapid development,as a unique biometric identification,face recognition has a wide range of applications in the social public security and daily life.Although the applications in a controlled environment has been able to basically meet the requirements,there are still many problems for the application under unrestricted conditions.Among these problems,how to extract effective facial features and design a robust classification algorithm are the two key problems to be solved for face recognition system.Based on the deep learning algorithm,this thesis makes a deep research on feature extraction and robust classification of face images,and proposes a new feature extraction algorithm combining the depth model with local patterns and a new classification method based on dynamic random forests.The mainly completed work as the following three parts:1)This paper studies a lot of face recognition algorithms,analyzes the problems existing in the process of feature extraction and classification,elaborates the development of deep learning,reveals the advantages of deep learning relative to shallow learning,examples of commonly used models of deep learning,and introduces the principles of local quantized patterns,restricted boltzmann machine and deep belief networks in detail.2)This paper presents a novel face feature extraction method based on deep learning and local quantized patterns.When the features of face is extracted by deep learning model,the existing methods directly use the original pixels of face image as the network input,the network will learn some unfavorable feature representation in unconstrained environments where the image is affected by the change of expression,the difference of attitude and the intensity of illumination and so on.Moreover,in the real world,the number of labeled face samples is small and insufficient to be used as training samples to adjust the network structure.In order to solve the above problems,this paper first uses the local quantization model to extract low-level local features of the face images preprocessed by gabor filters,and then uses the intergral feature which is integrated by the local features as the visual layer input of deep belief network,trains the deep network,extracts high-level abstract features.Compared with the existing methods,our approach not only utilizes the local descriptor's powerful performance of image visualization,but also takes advantage of deep learning's automatic way in feature extraction,overcomes the defects of the deep network in the process of feature extraction,showing better effectiveness and universality than other methods.3)This paper optimizes and improves the structure of deep network,and proposes to use dynamic random forests as the classifier at the top of deep learning model,instead of the inherent support vector machine and softmax classifier.Firstly using the depth model proposed above to extract the abstract characteristics of the sample,and then input them into the dynamic random forests for classification,constitute a new depth classification model by this way.The experimental results show that the depth classification model proposed in this thesis improves the accuracy of face image recognition and has better robustness.