Varying face occlusion detection and iterative recovery for face recognition

Wang, Meng; Hu, Zhengping; Sun, Zhe; Zhao, Shuhuan; Sun, Ming

doi:10.1117/1.jei.26.3.033009

Cited by 12 publications

(3 citation statements)

References 25 publications

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“…Data Augmentation/ Recovery [15], [20], [21], [24], [26], [31], [35], [51], [54], [62], [64], [73], [78], [89], [90], [107], [112], [136], [140], [147], [183] Feature Extraction [1], [2], [11], [23], [27], [32], [33], [48], [49], [53], [60], [62], [65], [74], [80], [88], [93], [108], [109], [124], [125], [128], [134], [143], [148], [153], [162], [163], [169],…”

Section: Pipeline Category Publicationmentioning

confidence: 99%

“…The second characteristic models the error image, which is the difference between the occluded test face and the unoccluded training face of the same identity, as low-rank structural. Wang et al [159] proposed a method equipped with two stages: varying occlusion detection stage consisting of occlusion detection and elimination, and iterative recovery stage consisting of occluded parts being recovered and unoccluded parts being reserved. With the use of iteratively recovered strategy, this joint occlusion detecting and recovery method can produce good global features to benefit classification.…”

Section: Sparse Representation Classifiermentioning

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: Sparse Representation Classifiermentioning

confidence: 99%

A survey of face recognition techniques under occlusion

2021

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“…Rigid templates, deformable part models and DCNN. The popular viola-jones face detection model, Harr like feature and AdaBoost comes under the rigid template category which can be drop performance in real-time applications [6]. DPM based suited better in real-time application, but their computational complexity is too much.…”

Section: General Face Detectionmentioning

confidence: 99%

A Comprehensive Study on Occlusion Invariant Face Recognition under Face Mask Occlusions

Hemathilaka¹,

Aponso²

2021

MLAIJ

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The face mask is an essential sanitaryware in daily lives growing during the pandemic period and is a big threat to current face recognition systems. The masks destroy a lot of details in a large area of face, and it makes it difficult to recognize them even for humans. The evaluation report shows the difficulty well when recognizing masked faces. Rapid development and breakthrough of deep learning in the recent past have witnessed most promising results from face recognition algorithms. But they fail to perform far from satisfactory levels in the unconstrained environment during the challenges such as varying lighting conditions, low resolution, facial expressions, pose variation and occlusions. Facial occlusions are considered one of the most intractable problems. Especially when the occlusion occupies a large region of the face because it destroys lots of official features.

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