ADAPTIVE FACE RECOGNITION THROUGH SVM-BASED CLASSIFICATION TECHNIQUES

Abstract

This article explores the use of Support Vector Machine (SVM) based classification techniques in adaptive face recognition. The article provides a comprehensive overview of the concepts and principles of SVM classification, as well as a detailed analysis of how these techniques can be applied in the context of facial recognition. The authors review a range of existing approaches to adaptive face recognition using SVMs, including feature extraction and selection, model training and tuning, and evaluation metrics. They also discuss the challenges and limitations of SVM-based classification methods in this domain, and highlight potential future directions for research in the field. Overall, this article is a valuable resource for researchers and practitioners interested in improving the accuracy and efficiency of face recognition systems through the use of adaptive SVM-based classification techniques.