Face Recognition: Novel Comparison of Various Feature Extraction Techniques

被引:5
|
作者
Makhija, Yashoda [1 ]
Sharma, Rama Shankar [1 ]
机构
[1] Rajasthan Tech Univ, Kota, Rajasthan, India
关键词
Feature extraction; Principal component analysis (PCA); Linear discriminant analysis (LDA); Elastic bunch graph matching (EBGM); Local binary pattern histogram (LBPH); EIGENFACES;
D O I
10.1007/978-981-13-0761-4_110
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Face recognition system certainly recognizes a face in a picture. This involves extracting features of an image and then recognizing it, despite lighting, expression, pose, aging, and transformations (translate, rotate, and scale image) which is a tough task. In the following research paper, a comprehensive literature review of various kinds of technologies for feature extraction is listed. To present a comprehensive review, we classify residing feature extraction technologies along with detailed description of specific approaches within each classification. These strategies are grouped into four noteworthy classifications, specifically, feature-based, appearance-based, template-based, and part-based approaches. The motivation for our work is the unavailability of comprehensive and direct independent comparison of each one of the feasible algorithm executions in the previously available survey. After considerable exploration of these strategies, we analyze that various feature extraction technologies provide leading results for various applications of image processing.
引用
收藏
页码:1189 / 1198
页数:10
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