An improvement on face recognition rate using local tetra patterns with support vector machine under varying illumination conditions

Juneja, Komal; Verma, Anju; Goel, Swati

doi:10.1109/ccaa.2015.7148566

Cited by 4 publications

(1 citation statement)

References 20 publications

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“…It is mostly used in Content-Based Image Retrieval (CBIR). It was found that the results obtained by second-order LTrP are better than higher-order LTrP because the sensitivity to noise increases as the order gets increased [17]. Mehmood et al [18] presented a novel image representation technique based on the Bag of Visual Words (BoVW) model in which an image is partitioned into two rectangular regions and then histograms of those regions are calculated.…”

Section: Related Workmentioning

confidence: 99%

Local-Tetra-Patterns for Face Recognition Encoded on Spatial Pyramid Matching

Khayam¹,

Mehmood²,

Chaudhry³

et al. 2022

Computers, Materials &Amp; Continua

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Face recognition is a big challenge in the research field with a lot of problems like misalignment, illumination changes, pose variations, occlusion, and expressions. Providing a single solution to solve all these problems at a time is a challenging task. We have put some effort to provide a solution to solving all these issues by introducing a face recognition model based on local tetra patterns and spatial pyramid matching. The technique is based on a procedure where the input image is passed through an algorithm that extracts local features by using spatial pyramid matching and max-pooling. Finally, the input image is recognized using a robust kernel representation method using extracted features. The qualitative and quantitative analysis of the proposed method is carried on benchmark image datasets. Experimental results showed that the proposed method performs better in terms of standard performance evaluation parameters as compared to state-of-the-art methods on AR, ORL, LFW, and FERET face recognition datasets.

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Section: Related Workmentioning

confidence: 99%