A Novel Statistical Feature Analysis-Based Global and Local Method for Face Recognition
Joint Authors
Talab, Mohammed Ahmed
Awang, Suryanti
Ansari, Mohd Dilshad
Source
International Journal of Optics
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-06-01
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
Face recognition from an image/video has been a fast-growing area in research community, and a sizeable number of face recognition techniques based on texture analysis have been developed in the past few years.
Further, these techniques work well on gray-scale and colored images, but very few techniques deal with binary and low-resolution images.
As the binary image is becoming the preferred format for low face resolution analysis, there is a need for further studies to provide a complete solution for the image-based face recognition system with a higher accuracy rate.
To overcome the limitation of the existing methods in extracting distinctive features in low-resolution images due to the contrast between the face and background, we propose a statistical feature analysis technique to fill the gaps.
To achieve this, the proposed technique integrates the binary-level occurrence matrix (BLCM) and the fuzzy local binary pattern (FLBP) named FBLCM to extract global and local features of the face from binary and low-resolution images.
The purpose of FBLCM is to distinctively improve performance of edge sharpness between black and white pixels in the binary image and to extract significant data relating to the features of the face pattern.
Experimental results on Yale and FEI datasets validate the superiority of the proposed technique over the other top-performing feature analysis methods.
The developed technique has achieved the accuracy of 94.54% when a random forest classifier is used, hence outperforming other techniques such as the gray-level co-occurrence matrix (GLCM), bag of word (BOW), and fuzzy local binary pattern (FLBP), respectively.
American Psychological Association (APA)
Talab, Mohammed Ahmed& Awang, Suryanti& Ansari, Mohd Dilshad. 2020. A Novel Statistical Feature Analysis-Based Global and Local Method for Face Recognition. International Journal of Optics،Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1172952
Modern Language Association (MLA)
Talab, Mohammed Ahmed…[et al.]. A Novel Statistical Feature Analysis-Based Global and Local Method for Face Recognition. International Journal of Optics No. 2020 (2020), pp.1-17.
https://search.emarefa.net/detail/BIM-1172952
American Medical Association (AMA)
Talab, Mohammed Ahmed& Awang, Suryanti& Ansari, Mohd Dilshad. A Novel Statistical Feature Analysis-Based Global and Local Method for Face Recognition. International Journal of Optics. 2020. Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1172952
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1172952