Research on Artificial Intelligence and Face Recognition

With the rapid progress of artificial intelligence technologies, face recognition has emerged as a critical domain within biometrics. The primary objective of face recognition technology is to facilitate rapid identification and authentication of individuals by analyzing facial image features. This technology has been widely applied in various sectors, including security surveillance, financial transactions, education, healthcare, and more. This paper presents a comprehensive overview and analysis of contemporary face recognition methodologies, including geometric feature extraction, the combination of Wavelet transform and Principal Component Analysis (PCA) in artificial neural networks, elastic graph matching, Linear Discriminant Hashing (LHD), and Support Vector Machines (SVM). Through a comparative evaluation of these methods, this study clarifies their respective advantages and limitations. The findings indicate that face recognition technology basically consists of essential components such as face detection, image processing, feature extraction, feature comparison, and decision-making for recognition. Although there are similarities in the general workflow among different methodologies, each approach optimizes specific aspects to varying extents. As a result, every method has its own distinct strengths and weaknesses. Therefore, choosing the most suitable technique for practical applications usually depends on specific requirements and contextual conditions. X