Face Recognition and Human Tracking Using GMM, HOG and SVM in Surveillance Videos

Harihara Santosh Dadi (), Gopala Krishna Mohan Pillutla and Madhavi Latha Makkena
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Harihara Santosh Dadi: JNT University
Gopala Krishna Mohan Pillutla: Institute of Aeronautical Engineering
Madhavi Latha Makkena: JNT University

Annals of Data Science, 2018, vol. 5, issue 2, No 3, 157-179

Abstract: Abstract Tracking of human and recognition in public places using surveillance cameras is the topic of research in the area computer vision. Recognition of human and then tracking completes the video surveillance system. A novel algorithm for face recognition and human tracking is presented in this article. Human is tracked using Gaussian mixture model. To track the human in specific, template of GMM is divided into four regions which are placed one above the other and tracked simultaneously. For recognizing the human, the histogram of oriented gradients features of the face region are given to the support vector machine classifier. Three experiments are conducted in taking the training faces. Every $$10{\mathrm{th}}$$ 10 th frame, every $$5{\mathrm{th}}$$ 5 th frame and every $$3{\mathrm{rd}}$$ 3 rd frame of the first 100 frames are considered. The other frames in the video are considered for testing using SVM classifier. Three datasets namely AITAM1 (simple), AITAM2 (moderate) and AITAM3 (complex) are used in this work. The experimental results show that as the complexity of dataset increases the performance metrics are getting decreased. The more the number of training faces in preparing a classifier, the better is the face recognition rate. This is experimented for all types of datasets. The Performance results show that the combination of the tracking algorithm and the face recognition algorithm not only tracks the person but also recognizes the person. This unique property of both tracking and recognition makes it best suit for video surveillance applications.

Keywords: Gaussian mixture model; Support vector machine; Histogram of oriented gradients (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s40745-017-0123-2

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