基于双层异构深度神经网络模型的人脸识别关键技术研究

伴随着人工智能的快速发展,人脸识别技术在社会领域和工业领域都呈现出较广泛的应用潜力空间,但由于传统人脸识别技术识别率低,识别速度慢,对环境要求非常高,迫切需要革新方法.本文旨在研究如何将深度学习算法引入人脸识别领域,通过构建双层异构深度神经网络模型,模拟神经网络进行学习,使用CNN与DBN等众多模型让计算机逐渐根据大量数据特征学会识别图像与人脸,并对人脸识别领域关键技术难点进行深入研究,从而大幅度提升人脸识别技术的识别率与鲁棒性.

SResearch on key technologies of face recognition based on double -layer heterogeneous depth neural network model

With the rapid development of artificial intelligence, face recognition technology has been widely used in the social and industrial fields . But the traditional face recognition technology has low recognition rate, slow recognition speed and very high environmental requirements. Therefore, it is urgent to innovate the method. This paper aims to study how to deep learning algorithm in face recognition field, by constructing a double heterogeneous depth of the neural network model, simulation of neural network learning, the use of CNN and DBN and many other models make the computer gradually learn to recognize image features based on a large number of data and human face, and in-depth study of the key technologies of face recognition, and the recognition rate to improve the robustness of face recognition technology.