Development of Algorithm for Person Re-Identification Using Extended Openface Method  

作　　者：S.Michael Dinesh A.R.Kavitha 

机构地区：[1]Anna University,Chennai,600025,India [2]Department of Computer Science&Engineering,SRM Institute of Science&Technology,Chennai,600026,India

出　　处：《Computer Systems Science & Engineering》2023年第1期545-561,共17页计算机系统科学与工程（英文）

摘　　要：Deep learning has risen in popularity as a face recognition technology in recent years.Facenet,a deep convolutional neural network(DCNN)developed by Google,recognizes faces with 128 bytes per face.It also claims to have achieved 99.96%on the reputed Labelled Faces in the Wild(LFW)dataset.How-ever,the accuracy and validation rate of Facenet drops down eventually,there is a gradual decrease in the resolution of the images.This research paper aims at developing a new facial recognition system that can produce a higher accuracy rate and validation rate on low-resolution face images.The proposed system Extended Openface performs facial recognition by using three different features i)facial landmark ii)head pose iii)eye gaze.It extracts facial landmark detection using Scattered Gated Expert Network Constrained Local Model(SGEN-CLM).It also detects the head pose and eye gaze using Enhanced Constrained Local Neur-alfield(ECLNF).Extended openface employs a simple Support Vector Machine(SVM)for training and testing the face images.The system’s performance is assessed on low-resolution datasets like LFW,Indian Movie Face Database(IMFDB).The results demonstrated that Extended Openface has a better accuracy rate(12%)and validation rate(22%)than Facenet on low-resolution images.

关 键 词：Constrained local model enhanced constrained local neuralfield enhanced hog scattered gated expert network support vector machine 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]