M. Jayasree1, K. A. Sunitha2,*, A. Brindha1, Punna Rajasekhar3, G. Aravamuthan3, G. Joselin Retnakumar1
Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 745-764, 2024, DOI:10.32604/iasc.2024.052983
- 06 September 2024
Abstract Identifying faces in non-frontal poses presents a significant challenge for face recognition (FR) systems. In this study, we delved into the impact of yaw pose variations on these systems and devised a robust method for detecting faces across a wide range of angles from 0° to ±90°. We initially selected the most suitable feature vector size by integrating the Dlib, FaceNet (Inception-v2), and “Support Vector Machines (SVM)” + “K-nearest neighbors (KNN)” algorithms. To train and evaluate this feature vector, we used two datasets: the “Labeled Faces in the Wild (LFW)” benchmark data and the “Robust… More >
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