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基于图像特征补偿和改进的 PSO 相结合的人脸识别算法。

A face recognition algorithm based on the combine of image feature compensation and improved PSO.

机构信息

Guangdong Songshan Polytechnic, Shaoguan, 512126, Guangdong, China.

出版信息

Sci Rep. 2023 Jul 31;13(1):12372. doi: 10.1038/s41598-023-39607-3.

DOI:10.1038/s41598-023-39607-3
PMID:37524837
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10390551/
Abstract

Face recognition systems have been widely applied in various scenarios in people's daily lives. The recognition rate and speed of face recognition systems have always been the two key technical factors that researchers focus on. Many excellent recognition algorithms achieve high recognition rates or good recognition speeds. However, more research is needed to develop algorithms that can effectively balance these two indicators. In this study, we introduce an improved particle swarm optimization algorithm into a face recognition algorithm based on image feature compensation techniques. This allows the system to achieve high recognition rates while simultaneously enhancing the recognition efficiency, aiming to strike a balance between the two aspects. This approach provides a new perspective for the application of image feature compensation techniques in face recognition systems. It helps achieve a broader range of applications for face recognition technology by reducing the recognition speed as much as possible while maintaining a satisfactory recognition rate. Ultimately, this leads to an improved user experience.

摘要

人脸识别系统已广泛应用于人们日常生活中的各种场景。人脸识别系统的识别率和速度一直是研究人员关注的两个关键技术因素。许多优秀的识别算法实现了高识别率或良好的识别速度。然而，仍需要更多的研究来开发能够有效平衡这两个指标的算法。在这项研究中，我们将改进的粒子群优化算法引入到基于图像特征补偿技术的人脸识别算法中。这使得系统能够在提高识别率的同时提高识别效率，从而在这两个方面实现平衡。这种方法为图像特征补偿技术在人脸识别系统中的应用提供了新的视角。通过在保持满意的识别率的同时尽可能地降低识别速度，该方法有助于人脸识别技术实现更广泛的应用，从而提高用户体验。

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