Performance Analysis of Pose Invariant Face Recognition Approaches in Unconstrained Environments

Performance Analysis of Pose Invariant Face Recognition Approaches in Unconstrained Environments

M. Parisa Beham (Department of Electronics and Communication Engineering, Vickram College of Engineering, Madurai, India), S. M. Mansoor Roomi (Department of Electronics and Communication Engineering, Thiagarajar College of Engineering, Madurai, India), J. Alageshan (Department of Electronics and Communication Engineering, Vickram College of Engineering, Madurai, India) and V. Kapileshwaran (Department of Electronics and Communication Engineering, Vickram College of Engineering, Madurai, India)
Copyright: © 2015 |Pages: 16
DOI: 10.4018/IJCVIP.2015010104
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Abstract

Face recognition and authentication are two significant and dynamic research issues in computer vision applications. There are many factors that should be accounted for face recognition; among them pose variation is a major challenge which severely influence in the performance of face recognition. In order to improve the performance, several research methods have been developed to perform the face recognition process with pose invariant conditions in constrained and unconstrained environments. In this paper, the authors analyzed the performance of a popular texture descriptors viz., Local Binary Pattern, Local Derivative Pattern and Histograms of Oriented Gradients for pose invariant problem. State of the art preprocessing techniques such as Discrete Cosine Transform, Difference of Gaussian, Multi Scale Retinex and Gradient face have also been applied before feature extraction. In the recognition phase K- nearest neighbor classifier is used to accomplish the classification task. To evaluate the efficiency of pose invariant face recognition algorithm three publicly available databases viz. UMIST, ORL and LFW datasets have been used. The above said databases have very wide pose variations and it is proved that the state of the art method is efficient only in constrained situations.
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The non-intrusive nature of the face biometric makes it superior to other biometrics. Therefore, face is one of the most suitable biometrics for surveillance applications. However, in typical surveillance scenarios, people usually walk free, and it is not always possible to capture the frontal faces. This leads to a problem in face recognition, unconstrained face recognition. Although there have been some early successes in automatic face recognition, it is still far from being completely solved, especially in uncontrolled environments. Figure 1 addresses the difficult problem of identifying a face taken in uncontrolled situations with different view point using a large dictionary of still face images of thousand people. Among this, pose variations can be considered as one of the most important and challenging problems in face recognition. As the viewpoint varies, the 2D facial appearance will change because the human head has a complex non planar geometry.

Figure 1.

Overall block diagram of face recognition methodology

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