Biometrics and Bioinformatics

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Real Time Face Recognition Using Adaboost with HAAR Cascade Features and Support Vector Machines


Affiliations
1 Dept.of E & C, S.J.C. Institute of Technology, Chickballapur, Karnataka, India
2 Dept. of E & C, Siddaganga Institute of Technology, Tumkur, Karnataka, India
     

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This paper presents a real time face recognition system for human face recognition in a real time background for a large database of persons face. The task of real time face recognition is challenging because the current method are still susceptible to the illumination condition, pose, occlusion and expression. The proposed system collapses most of this variance. In this paper, we propose to use AdaBoost with Haar cascade features for face detection and then in order to recognize the detected faces, SVM (support vector machine) is used. Further a comparative analysis of SVM (Support vector machines), Linear discriminate analysis (LDA) and Principal component analysis (PCA) are also made. The experimental results show that the SVM algorithm has quite good performance in terms of real-time and accuracy.

Keywords

Face Recognition, Eigenface, AdaBoost, Haar Cascade Classifier, Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA).
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  • Real Time Face Recognition Using Adaboost with HAAR Cascade Features and Support Vector Machines

Abstract Views: 145  |  PDF Views: 3

Authors

N. Tilakraj
Dept.of E & C, S.J.C. Institute of Technology, Chickballapur, Karnataka, India
R. Srikantaswamy
Dept. of E & C, Siddaganga Institute of Technology, Tumkur, Karnataka, India

Abstract


This paper presents a real time face recognition system for human face recognition in a real time background for a large database of persons face. The task of real time face recognition is challenging because the current method are still susceptible to the illumination condition, pose, occlusion and expression. The proposed system collapses most of this variance. In this paper, we propose to use AdaBoost with Haar cascade features for face detection and then in order to recognize the detected faces, SVM (support vector machine) is used. Further a comparative analysis of SVM (Support vector machines), Linear discriminate analysis (LDA) and Principal component analysis (PCA) are also made. The experimental results show that the SVM algorithm has quite good performance in terms of real-time and accuracy.

Keywords


Face Recognition, Eigenface, AdaBoost, Haar Cascade Classifier, Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA).