Development of Partial Face Recognition Framework

Neo, Han Foon; Teo, Chuan Chin; Teoh, Andrew Beng Jin

doi:10.1109/cgiv.2010.29

Cited by 13 publications

(6 citation statements)

References 10 publications

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“…Ref. [133] compares several subspace‐based methods including PCA, non‐negative matrix factorization (NMF) [134], local NMF [135] and spatially confined NMF [136] and uses the partial information available for face recognition. During face recognition, the eye region is selected when people are wearing masks or veils, and the bottom region is used when people are wearing glasses.…”

Section: Occlusion Aware Face Recognitionmentioning

confidence: 99%

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: Occlusion Aware Face Recognitionmentioning

confidence: 99%

A survey of face recognition techniques under occlusion

2021

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“…Reference [11] introduces a selective local non‐negative matrix factorization (NMF) method to select features corresponding to occlusion‐free regions for recognition. Another work [12] extends NMF to include occlusion estimation adaptively according to reconstruction errors. Finally, low‐dimensional representations are learnt to ensure that features of the same class are close to the corresponding class centre.…”

Section: Related Workmentioning

confidence: 99%

Occlusion‐invariant face recognition using simultaneous segmentation

et al. 2021

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When using convolutional neural network (CNN) models to extract features of an occluded face, the occluded part will inevitably be embedded into the representation just as with other facial regions. Existing methods deal with occluded face recognition either by augmenting the training dataset with synthesized occluded faces or by segmenting occlusions first and subsequently recognize the face based on unoccluded facial regions. Instead, simultaneous occlusion segmentation and face recognition is developed to make the most of these correlated two tasks. This is inspired by the phenomenon that features corrupted by occlusion are traceable within a CNN trained to segment occluded parts in face images. Specifically, a simultaneous occlusion invariant deep network (SOIDN) is proposed that contains simultaneously operating face recognition and occlusion segmentation networks coupled with an occlusion mask adaptor module as their bridge to learn occlusion invariant features. The training of SOIDN is jointly supervised by classification and segmentation losses aiming to obtain (1) occlusion invariant features, (2) occlusion segmentation, and (3) an occlusion feature mask that weighs the reliability of features. Experiments on synthesized occluded dataset (e.g. LFW-occ) and real occluded face dataset (e.g. AR) demonstrate that SOIDN outperforms state of the art methods for face verification and identification.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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“…However, the computational cost of each algorithms is expensive and the required alignment step limits its practical applications. Besides, region-based models [3], [9], [23], [24], [25], [32], [33] also offered a solution for partial face recognition. They only required face sub-regions as input, such as eye [32], nose [32], half (left or right portion) of the face [9], or the periocular region [26].…”

Section: Partial Face Recognitionmentioning

confidence: 99%

Recognizing Partial Biometric Patterns

He,

Sun,

Zhu

et al. 2018

Preprint

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Biometric recognition on partial captured targets is challenging, where only several partial observations of objects are available for matching. In this area, deep learning based methods are widely applied to match these partial captured objects caused by occlusions, variations of postures or just partial out of view in person re-identification and partial face recognition. However, most current methods are not able to identify an individual in case that some parts of the object are not obtainable, while the rest are specialized to certain constrained scenarios. To this end, we propose a robust general framework for arbitrary biometric matching scenarios without the limitations of alignment as well as the size of inputs. We introduce a feature post-processing step to handle the feature maps from FCN and a dictionary learning based Spatial Feature Reconstruction (SFR) to match different sized feature maps in this work. Moreover, the batch hard triplet loss function is applied to optimize the model. The applicability and effectiveness of the proposed method are demonstrated by the results from experiments on three person re-identification datasets (Market1501, CUHK03, DukeMTMC-reID), two partial person datasets (Partial REID and Partial iLIDS) and two partial face datasets (CASIA-NIR-Distance and Partial LFW), on which state-of-the-art performance is ensured in comparison with several state-of-the-art approaches. The code is released online and can be found on the website: https://github.com/lingxiao-he/Partial-Person-ReID.

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Development of Partial Face Recognition Framework

Cited by 13 publications

References 10 publications

A survey of face recognition techniques under occlusion

A survey of face recognition techniques under occlusion

Occlusion‐invariant face recognition using simultaneous segmentation

Recognizing Partial Biometric Patterns

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