Local Binary Pattern Based ELM for Face Identification

Ahuja, Bhawna; Vishwakarma, Virendra P.

doi:10.1007/978-981-15-4992-2_34

Cited by 10 publications

(1 citation statement)

References 19 publications

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“…The inclusion of Deep learning methods will definitely enhances the recognition rate. Ahuja et al [22] presented the FR by using the combination of LBP and ELM. In LBP, the features are derived from distinct mic-regions and then all regions features are integrated to develop the entire image representation.…”

Section: Related Workmentioning

confidence: 99%

Radial Orthogonal Median LBP (ROM-LBP): A Discriminant Local Descriptor in Light Variations for Face Recognition

Karanwal

Diwakar

2023

JGEU

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LBP and majority of its variants performs extremely well in front of moderate light variations. But when light variations becomes severe then performance of LBP and its variants is not satisfactory. Therefore there is a need of the more promising and impressive descriptor which performs well in harsh light variations. To complement these LBP based descriptors the proposed work launches the novel descriptor for Face Recognition (FR) in harsh lightning variations. This proposed descriptor is called as Radial Orthogonal Median LBP (ROM-LBP). The main demerit of these LBP based descriptors is that they all consider the uniform coordination between the neighbors and center pixel. Which mean raw pixel intensity is used for the comparison with the center pixel. The proposed work eliminates this problem in the introduced descriptor ROM-LBP, by replacing the raw pixels intensity with the median of the radial points in each orthogonal position of the two separate groups. The generated median is then used for comparison with the center pixel. The respective codes obtained from both the groups are concatenated to form the ROM-LBP size. As region feature extraction is done therefore ROM-LBP develops the large feature size. To make more effective descriptor, the services of FLDA is used and then classification was conducted by SVMs. Experiments conducted on EYB and YB datasets demonstrates the ability of the proposed ROM-LBP against various LBP and non-LBP based descriptors.

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Section: Related Workmentioning

confidence: 99%