Overview and Exploration of Standardization in Facial Recognition System Evaluation Technology

Abstract

This article provides an overview of the development of face recognition technology, evaluation techniques, and the exploration of standardization. Face recognition technology, as a biometric identification method, identifies individuals by analyzing facial features in images or videos and has been widely applied in various fields such as public safety, finance, and community management. With the advancement of technology, the market size of face recognition has grown rapidly, while facing complex recognition challenges. The article reviews the historical development of face recognition algorithms, including traditional Eigenface algorithms, Fisherface algorithms, elastic graph matching algorithms, local feature analysis algorithms, and the application of modern neural network algorithms, especially convolutional neural networks. In addition, the importance of large-scale face image databases in the performance testing of face recognition systems is discussed, and internationally renowned face databases such as FERET, CMU, LFW, etc., are introduced. The article also explores the progress of international and domestic face recognition system performance testing projects, emphasizing the fairness and authority of testing, and proposes directions and suggestions for standardization research in this field in China.