A Deep Transfer Learning Approach for Addressing Yaw Pose Variation to Improve Face Recognition Performance  

作　　者：M.Jayasree K.A.Sunitha A.Brindha Punna Rajasekhar G.Aravamuthan G.Joselin Retnakumar 

机构地区：[1]Department of Electronics and Instrumentation Engineering,College of Engineering and Technology,SRM Institute of Science and Technology,Kattankulathur,Chennai,Tamil Nadu,603203,India [2]Department of Electronics and Communication Engineering,SRM University,Amaravati,Mangalagiri,Andhra Pradesh,522502,India [3]Security Electronics and Cyber Technology,Bhabha Atomic Research Centre,Anushakti Nagar,Mumbai,Maharashtra,400085,India

出　　处：《Intelligent Automation & Soft Computing》2024年第4期745-764,共20页智能自动化与软计算（英文）

基　　金：funding for the project,excluding research publication,from the Board of Research in Nuclear Sciences(BRNS)under Grant Number 59/14/05/2019/BRNS.

摘　　要：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.

关 键 词：Face recognition pose variations transfer learning method yaw poses FaceNet Inception-v2 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]