Department of Design, Silla University, Busan 46958, Republic of Korea.
Comput Intell Neurosci. 2022 Jul 14;2022:8633846. doi: 10.1155/2022/8633846. eCollection 2022.
The rapid development of science and technology, i.e., integrated modules that are actuators and sensors, promotes the comprehensive popularization of intelligent products in people's life. More particularly, with the advent of the hybrid of the Internet of things and artificial intelligence, more and more activities preferably linked to the human beings have been automated and developed. Among those fields, intelligent face recognition has also become a basic technology in work and life. This technology has been widely used in various products and is well known by people. However, the intelligent face recognition system developed at present lacks universal design concept, and the designed system cannot be applied to various products. During the use of users, there are some problems, such as difficult operation and unfriendly interface. In order to improve the satisfaction of users' physical examination and the accuracy of intelligent face recognition, this study develops an intelligent face recognition system based on the universal design concept. First, the universal design concept is briefly described, and the calculation process of face detection algorithm and face detection algorithm based on the optical flow method is introduced in detail. Then, when designing a face recognition system, this algorithm is used to build a complete system framework. The main functional modules in this system are face detection module, face recognition module, and face training module. The functions of each module are described in detail. Finally, the face feature extraction results of the face detection algorithm based on the optical flow method are verified on the Yale face database and PIE face database. The results show that the algorithm has the highest detection and recognition rate. At the same time, the ORL face database is used to compare and analyze the system performance. The face image recognition rate of this algorithm is 92, which is the highest compared with other algorithms.
科学技术的快速发展，即集执行器和传感器于一体的集成模块，推动了智能产品在人们生活中的全面普及。更具体地说，随着物联网和人工智能的融合的出现，越来越多与人类相关的活动已经实现了自动化和发展。在这些领域中，智能人脸识别也已成为工作和生活中的一项基本技术。这项技术已广泛应用于各种产品，并为人们所熟知。然而，目前开发的智能人脸识别系统缺乏通用设计理念，设计的系统无法应用于各种产品。在用户使用过程中，存在一些问题，例如操作困难和界面不友好。为了提高用户体检的满意度和智能人脸识别的准确性，本研究基于通用设计理念开发了一种智能人脸识别系统。首先，简要描述了通用设计理念，并详细介绍了人脸检测算法和基于光流法的人脸检测算法的计算过程。然后，在设计人脸识别系统时，使用该算法构建了完整的系统框架。该系统的主要功能模块包括人脸检测模块、人脸识别模块和人脸训练模块。详细描述了每个模块的功能。最后，在 Yale 人脸数据库和 PIE 人脸数据库上验证了基于光流法的人脸检测算法的人脸特征提取结果。结果表明，该算法具有最高的检测和识别率。同时，还使用 ORL 人脸数据库对系统性能进行了比较和分析。该算法的人脸图像识别率为 92%，与其他算法相比最高。
