Random sampling for patch-based face recognition

Cheheb, Ismahane; Almaadeed, Noor; Al-Madeed, Somaya; Bouridane, Ahmed; Jiang, Richard

doi:10.1109/iwbf.2017.7935104

Cited by 20 publications

(11 citation statements)

References 19 publications

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“…Data augmentation/Recovery [90-92, 123-125, 150, 151, 153, 154, 159-164, 179, 182-184, 190] Feature extraction [71, 72, 75, 77, 78, 80, 93, 98-100, 102, 103, 105, 107, 109-114, 116, 117, 126, 137-139, 143, 147, 148, 151, 152, 156, 158] Feature comparison [82,83,101,104,128,129,131,133,[144][145][146] Fusion strategy [84,94,114,117,127,[140][141][142]157] Notes: In the 'data augmentation/recovery' category, data augmentation component means generating synthesised occluded faces while the data recovery component intends to eliminate the occluded facial part. The fusion strategy component consists of feature-level fusion as well as decision-level fusion.…”

Section: Pipeline Category Publicationmentioning

confidence: 99%

“…Application-oriented Purpose Publication OFD Occluded Face Detection [14], [18], [36], [61], [61], [91], [106], [120], [146], [155], [159], [165], [165]- [167], [174], [192] ORFE Patch based engineered features [1], [2], [12], [23], [57], [65], [88], [111], [122], [180], [181], [193] Learning based features [11], [27], [31]- [33], [53], [65], [73], [74], [90], [93], [96], [107], [109], [124], [125], [133], [134], [136], [148], [153], [162], ...…”

Section: Abbreviationmentioning

confidence: 99%

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A survey of face recognition techniques under occlusion

2021

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The limited capacity to recognise faces under occlusions is a long-standing problem that presents a unique challenge for face recognition systems and even humans. The problem regarding occlusion is less covered by research when compared to other challenges such as pose variation, different expressions, etc. Nevertheless, occluded face recognition is imperative to exploit the full potential of face recognition for real-world applications. In this article, the scope to occluded face recognition is restricted and a systematic categorisation that new as well as classic methods fit into is presented. First, the authors explore the kind of the occlusion problem and the type of inherent difficulties that can arise. As a part of this review, face detection under occlusion, a preliminary step in face recognition. Second the authors analyse how the existing face recognition methods cope with the occlusion problem and classify them into three categories, which are given as: 1) occlusion robust feature extraction approaches, 2) occlusion aware face recognition approaches, and 3) occlusion recovery based face recognition approaches. Furthermore, the motivations, innovations, pros and cons, and the performance of representative approaches for comparison are analyzed. Finally, future challenges and method trends of occluded face recognition are thoroughly discussed.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Pipeline Category Publicationmentioning

confidence: 99%

Section: Abbreviationmentioning

confidence: 99%

A survey of face recognition techniques under occlusion

2021

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“…The discriminative common vector technique has utilized the feature of variations of fisher's linear discriminate analysis for smallest query sample. Cheheb et al (2017) proposed an approach to solve the partial occlusion present in face recognition system using a single sample per person (SSPP). First, image is divided into multiple patches and local binary patterns are applied as a texture descriptor on each block separately.…”

Section: Related Workmentioning

confidence: 99%

Robust Face Recognition Under Partial Occlusion Based on Local Generic Features

Yadav

Gupta

Khan³

et al. 2021

International Journal of Cognitive Informatics and Natural Intelligence

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Face recognition has drawn significant attention due to its potential use in biometric authentication, surveillance, security, robotics, and so on. It is a challenging task in the field of computer vision. Although the various state-of-the-art methods of face recognition in constrained environments have achieved satisfactory results, there are still many issues which are untouched in unconstrained environments, such as partial occlusions, large pose variations, etc. In this paper, the authors have proposed an approach which utilized the local generic feature (LGF) to recognize the face in the partial occlusion by fusing features scale invariant feature transform (SIFT) and multi-block local binary pattern (MB-LBP). It also utilizes robust kernel method for classification of the query image. They have validated the effectiveness of the proposed approach on the benchmark AR face database. The experimental outcomes illustrate that the proposed approach outperformed the state-of-art methods for robust face recognition.

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“…There are also region-based or partial-based models for face recognition (Ou et al 2018) (Cheheb et al 2017) (He et al 2018). However, these approaches need to set apart the occluded regions of face images before feature extraction.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Graph Representation for Occlusion Handling in Biometrics

Ren

Wang²,

Sun³

et al. 2020

AAAI

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The generalization ability of Convolutional neural networks (CNNs) for biometrics drops greatly due to the adverse effects of various occlusions. To this end, we propose a novel unified framework integrated the merits of both CNNs and graphical models to learn dynamic graph representations for occlusion problems in biometrics, called Dynamic Graph Representation (DGR). Convolutional features onto certain regions are re-crafted by a graph generator to establish the connections among the spatial parts of biometrics and build Feature Graphs based on these node representations. Each node of Feature Graphs corresponds to a specific part of the input image and the edges express the spatial relationships between parts. By analyzing the similarities between the nodes, the framework is able to adaptively remove the nodes representing the occluded parts. During dynamic graph matching, we propose a novel strategy to measure the distances of both nodes and adjacent matrixes. In this way, the proposed method is more convincing than CNNs-based methods because the dynamic graph method implies a more illustrative and reasonable inference of the biometrics decision. Experiments conducted on iris and face demonstrate the superiority of the proposed framework, which boosts the accuracy of occluded biometrics recognition by a large margin comparing with baseline methods.

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Random sampling for patch-based face recognition

Cited by 20 publications

References 19 publications

A survey of face recognition techniques under occlusion

A survey of face recognition techniques under occlusion

Robust Face Recognition Under Partial Occlusion Based on Local Generic Features

Dynamic Graph Representation for Occlusion Handling in Biometrics

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