@Article{iasc.2024.052983,
AUTHOR = {M. Jayasree, K. A. Sunitha, A. Brindha, Punna Rajasekhar, G. Aravamuthan, G. Joselin Retnakumar},
TITLE = {A Deep Transfer Learning Approach for Addressing Yaw Pose Variation to Improve Face Recognition Performance},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {39},
YEAR = {2024},
NUMBER = {4},
PAGES = {745--764},
URL = {http://www.techscience.com/iasc/v39n4/57822},
ISSN = {2326-005X},
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 Shape-Based FR System (RSBFRS)” real-time data, which contained face images with varying yaw poses. After selecting the best feature vector, we developed a real-time FR system to handle yaw poses. The proposed FaceNet architecture achieved recognition accuracies of 99.7% and 99.8% for the LFW and RSBFRS datasets, respectively, with 128 feature vector dimensions and minimum Euclidean distance thresholds of 0.06 and 0.12. The FaceNet + SVM and FaceNet + KNN classifiers achieved classification accuracies of 99.26% and 99.44%, respectively. The 128-dimensional embedding vector showed the highest recognition rate among all dimensions. These results demonstrate the effectiveness of our proposed approach in enhancing FR accuracy, particularly in real-world scenarios with varying yaw poses.},
DOI = {10.32604/iasc.2024.052983}
}