Largest Matching Areas for Illumination and Occlusion Robust Face Recognition

McLaughlin, Niall; Ji, Ming; Crookes, Danny

doi:10.1109/tcyb.2016.2529300

Cited by 60 publications

(34 citation statements)

References 50 publications

(68 reference statements)

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“…From the table one can see that our method clearly compares favourably against some of the best performing algorithms. For example, our method achieves 98% performance which matches both [13] and [22] in the scarf occluded set. It is is to be noted that the authors [22] have use more than one training sample unlike our method which uses SSPP.…”

Section: Experiments and Analysismentioning

confidence: 94%

“…It is is to be noted that the authors [22] have use more than one training sample unlike our method which uses SSPP. [13] 96 ∼ 98 97 ∼ 98 DICW [22] 99.5 98…”

Section: Experiments and Analysismentioning

confidence: 99%

“…Even though this approach is very effective and has proved to be more robust to the challenges of illumination variations and partial occlusions, it is still lacking in terms of classification performances since one single classifier is constructed for all the image patches. In [13], the authors find the largest matching area at each point of the face. However, in order to work well with partial occlusions, this method goes through an occlusion de-emphasis step.…”

Section: Introductionmentioning

confidence: 99%

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Random sampling for patch-based face recognition

Cheheb

Almaadeed

Al-Madeed

et al. 2017

2017 5th International Workshop on Biometrics and Forensics (IWBF)

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Abstract-Real face recognition is a challenging problem especially when face images are subject to distortions. This paper presents an approach to tackle partial occlusion distortions present in real face recognition using a single training sample per person. First, original images are partitioned into multiple blocks and Local Binary Patterns are applied as a local descriptor on each block separately. Then, a dimensionality reduction of the resulting descriptors is carried out using Kernel Principle Component Analysis. Once done, a random sampling method is used to select patches at random and hence build several sub-SVM classifiers. Finally, the results from each sub-classifier are combined in order to increase the recognition performance. To demonstrate the usefulness of the approach, experiments were carried on the AR Face Database and obtained results have shown the effectiveness of our technique.

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Section: Experiments and Analysismentioning

confidence: 94%

“…It is is to be noted that the authors [22] have use more than one training sample unlike our method which uses SSPP. [13] 96 ∼ 98 97 ∼ 98 DICW [22] 99.5 98…”

Section: Experiments and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Random sampling for patch-based face recognition

Cheheb

Almaadeed

Al-Madeed

et al. 2017

2017 5th International Workshop on Biometrics and Forensics (IWBF)

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show abstract

“…The proposed approaches, GPCRC and its improved methods, were evaluated in three publicly available databases: Extended Yale B [40][41][42], CMU PIE [43,44], and LFW [45,46]. To show the effectiveness of the GPCRC, we compared our methods with four classical methods and their improved methods, namely, SRC [13], CRC [15], SSRC [14], and PCRC [16].…”

Section: Experimental Analysismentioning

confidence: 99%

“…To test the robustness of the proposed method on illumination, we used the classic Extended Yale B database [40][41][42], because faces from Extended Yale B database were acquired in different illumination conditions. The Extended Yale B database contains 38 human subjects under 9 poses and 64 illumination conditions.…”

Section: Extended Yale Bmentioning

confidence: 99%

Patch Based Collaborative Representation with Gabor Feature and Measurement Matrix for Face Recognition

Liu

et al. 2018

Mathematical Problems in Engineering

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In recent years, sparse representation based classification (SRC) has emerged as a popular technique in face recognition. Traditional SRC focuses on the role of the 1 -norm but ignores the impact of collaborative representation (CR), which employs all the training examples over all the classes to represent a test sample. Due to issues like expression, illumination, pose, and small sample size, face recognition still remains as a challenging problem. In this paper, we proposed a patch based collaborative representation method for face recognition via Gabor feature and measurement matrix. Using patch based collaborative representation, this method can solve the problem of the lack of accuracy for the linear representation of the small sample size. Compared with holistic features, the multiscale and multidirection Gabor feature shows more robustness. The usage of measurement matrix can reduce large data volume caused by Gabor feature. The experimental results on several popular face databases including Extended Yale B, CMU PIE, and LFW indicated that the proposed method is more competitive in robustness and accuracy than conventional SR and CR based methods.

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Recognition of Facial Images via Self-Organizing Landmark Location with Approximate Graph Matching

Arya

Rajput

2021

Lecture Notes in Electrical Engineering

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Largest Matching Areas for Illumination and Occlusion Robust Face Recognition

Cited by 60 publications

References 50 publications

Random sampling for patch-based face recognition

Random sampling for patch-based face recognition

Patch Based Collaborative Representation with Gabor Feature and Measurement Matrix for Face Recognition

Recognition of Facial Images via Self-Organizing Landmark Location with Approximate Graph Matching

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