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A Survey Paper For Face Recognition Technologies

Authors: Nitisha Tyagi*1 & Amit Asthana2;

A Survey Paper For Face Recognition Technologies

Abstract

The biometric is a study of human behavior and features. Face recognition is a technique of biometric. Various approaches are used for it. A survey for all these techniques is in this paper for analyzing various algorithms and methods. Face recognition is emerging branch of biometric for security as no faces can be defeated as a security approach. So, how we can recognize a face with the help of computers is given in this paper. Face Recognition plays a major role in Biometrics. Feature selection is a measure issue in face recognition. This paper proposes a survey on face recognition. There are many methods to extract face features. In some advanced methods it can be extracted faster in a single scan through the raw image and lie in a lower dimensional space, but still retaining facial information efficiently. The methods which are used to extract features are robust to low-resolution images. The method is a trainable system for selecting face features. After the feature selection procedure next procedure is matching for face recognition. The recognition accuracy is increased by advanced methods.

Keywords

Face features, feature selection, local binary pattern. Face features, feature selection, local binary pattern.

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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