In recent years, with the continuous breakthrough of computer vision technology, the accuracy of object detection and target recognition has been improved by leaps and bounds. Face recognition is one of the important research directions in the field of computer vision, which is widely used in mobile payment, safe city, criminal investigation and other fields. Traditional face recognition methods need to extract face image features manually. The extracted features are greatly affected by subjective factors, and time-consuming and laborious. Deep learning is the most important technology in the field of computer vision at present. Compared with traditional face recognition methods, it can extract more essential features of face image without manual participation. In this paper, we build a face recognition system based on neural computing model and the principle of neural network. The experimental results show that the proposed method has high detection rate and short processing time.

近年来，随着计算机视觉技术的不断突破，物体检测和目标识别的准确性得到了突飞猛进的提高。人脸识别是计算机视觉领域的重要研究方向之一，广泛应用于移动支付，安全城市，刑事侦查等领域。传统的人脸识别方法需要手动提取人脸图像特征。所提取的特征受主观因素的影响很大，并且既费时又费力。深度学习是当前计算机视觉领域最重要的技术。与传统的人脸识别方法相比，它无需人工参与即可提取出更多的人脸图像基本特征。在本文中，我们建立了基于神经计算模型和神经网络原理的人脸识别系统。实验结果表明，该方法具有较高的检测率和较短的处理时间。