M. Jayasree¹, K. A. Sunitha^2,*, 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

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 >

Amit Verma¹, Mohammed Baljon², Shailendra Mishra^2,*, Iqbaldeep Kaur¹, Ritika Saini¹, Sharad Saxena³, Sanjay Kumar Sharma⁴

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1955-1974, 2022, DOI:10.32604/cmc.2022.020084

Abstract Biometric applications widely use the face as a component for recognition and automatic detection. Face rotation is a variable component and makes face detection a complex and challenging task with varied angles and rotation. This problem has been investigated, and a novice algorithm, namely RIFDS (Rotation Invariant Face Detection System), has been devised. The objective of the paper is to implement a robust method for face detection taken at various angle. Further to achieve better results than known algorithms for face detection. In RIFDS Polar Harmonic Transforms (PHT) technique is combined with Multi-Block Local Binary… More >