2020
DOI: 10.20944/preprints202012.0237.v1
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Multi-Block Color-Binarized Statistical Images for Single Sample Face Recognition

Abstract: Single sample face recognition (SSFR) is a computer vision challenge. In this scenario, there is only one example from each individual on which to train the system, making it difficult to identify persons in unconstrained environments, particularly when dealing with changes in facial expression, posture, lighting, and occlusion. This paper suggests a different method based on a variant of the Binarized Statistical Image Features (BSIF) descriptor called Multi-Block Color-Binarized Statistical Image Features (M… Show more

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Cited by 7 publications
(9 citation statements)
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“…Due to the robustness and the speed of convergence of DCSR algorithm, its application is efficient in vital and sensitive domains such as medical imaging and remote sensing. Our future contribution is a technological breakthrough consisting in introducing a layer of intelligence at the acquisition level aiming at automatically determining the image texture and its quality in terms of noise level, blur and shooting conditions (lighting, inpainting, registration, occlusion, low resolution, etc) [44][45][46][47] in order to automatically adjust the parameters necessary for an optimal use of the proposed DCSR algorithm.…”
Section: Discussionmentioning
confidence: 99%
“…Due to the robustness and the speed of convergence of DCSR algorithm, its application is efficient in vital and sensitive domains such as medical imaging and remote sensing. Our future contribution is a technological breakthrough consisting in introducing a layer of intelligence at the acquisition level aiming at automatically determining the image texture and its quality in terms of noise level, blur and shooting conditions (lighting, inpainting, registration, occlusion, low resolution, etc) [44][45][46][47] in order to automatically adjust the parameters necessary for an optimal use of the proposed DCSR algorithm.…”
Section: Discussionmentioning
confidence: 99%
“…There is a 3D model method that offers an excellent portrayal of the face shape for a good differentiation between people; they are regularly not appropriate for constant applications since they require costly and complex computations and an explicit number of sensors. The framework presented in [23] depends on image processing techniques like filtering and histogram calculations needing less computational cost than existing frameworks. The work had a calming accuracy up to 96%.…”
Section: Manish Et Al In the Papermentioning
confidence: 99%
“…Up to date, four main kinds of methods for SSPP face recognition are reported, including virtual sample generation [19][20][21], classifier [22][23][24], image partitioning [25][26][27][28][29] and generic learning [30][31][32][33][34][35][36][37][38][39][40]. The virtual sample generation method uses the singular value decomposition (SVD) [19], pose and illumination variability [20], the lower-upper decomposition [21] etc.…”
Section: Introductionmentioning
confidence: 99%
“…Furthermore, Yan et al [27] and Zhang et al [28] proposed the manifold learning methods, first, the feature is extracted for each image patch, then the graph structure of each image is obtained using its patch features, and finally the recognition result is obtained by matching the graph structure. Adjabi et al [29] proposed an original method based on multi-block color-binarized statistical image features to handle SSPP problem, which method has faster running speed. These methods use the similaritiy of patches in the same position to identify a probe patch.…”
Section: Introductionmentioning
confidence: 99%
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