Improving the Face Recognition Accuracy under Varying Illumination Conditions for Local Binary Patterns and Local Ternary Patterns Based on Weber-Face and Singular Value Decomposition

Tran, Chi-Kien; Tseng, Chin‐Dar; Lee, Tsair-Fwu

doi:10.1109/gtsd.2016.10

Cited by 11 publications

(5 citation statements)

References 17 publications

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“…The effect of variations in condition on face recognition has been investigated by authors [28]. Consequently, the dominant method has been the appearance-based method.…”

Section: Literature Reviewmentioning

confidence: 99%

[Retracted] Optimum Feature Selection with Particle Swarm Optimization to Face Recognition System Using Gabor Wavelet Transform and Deep Learning

Ahmed

Frikha

Hussein

et al. 2021

BioMed Research International

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In this study, Gabor wavelet transform on the strength of deep learning which is a new approach for the symmetry face database is presented. A proposed face recognition system was developed to be used for different purposes. We used Gabor wavelet transform for feature extraction of symmetry face training data, and then, we used the deep learning method for recognition. We implemented and evaluated the proposed method on ORL and YALE databases with MATLAB 2020a. Moreover, the same experiments were conducted applying particle swarm optimization (PSO) for the feature selection approach. The implementation of Gabor wavelet feature extraction with a high number of training image samples has proved to be more effective than other methods in our study. The recognition rate when implementing the PSO methods on the ORL database is 85.42% while it is 92% with the three methods on the YALE database. However, the use of the PSO algorithm has increased the accuracy rate to 96.22% for the ORL database and 94.66% for the YALE database.

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“…The effect of variations in condition on face recognition has been investigated by authors [28]. Consequently, the dominant method has been the appearance-based method.…”

Section: Literature Reviewmentioning

confidence: 99%

[Retracted] Optimum Feature Selection with Particle Swarm Optimization to Face Recognition System Using Gabor Wavelet Transform and Deep Learning

Ahmed

Frikha

Hussein

et al. 2021

BioMed Research International

View full text Add to dashboard Cite

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“…Although there exists many studies [14,27,28] on invariant representations for handling certain variations, apparently, a generic approach to model different variations at once, has not yet come to light. It has been known for a long time that feature-based methods, such as elastic bunch graph matching, are promisingly successful against many factors, including variations of illumination and viewpoint [29].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Nevertheless, their extreme sensitivity to feature extraction and the measurement of extracted features makes them unreliable [30]. Many authors have studied the effect of variations in illumination conditions on FR [14,28,[30][31][32][33]. As a result, appearance-based methods have dominated the literature.…”

Section: Literature Reviewmentioning

confidence: 99%

Face Recognition with Symmetrical Face Training Samples Based on Local Binary Patterns and the Gabor Filter

2019

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In the practical reality of face recognition applications, the human face can have only a limited number of training images. However, it is known that, in general, increasing the number of training images also increases the performance of face recognition systems. In this case, a new set of training samples can be generated from the original samples, using the symmetry property of the face. Although many face recognition methods have been proposed in the literature, a robust face recognition system is still a challenging task. In this paper, recognition performance was improved by using the property of face symmetry. Moreover, the effects of illumination and pose variations were reduced. A Two-Dimensional Discrete Wavelet Transform, based on the Local Binary Pattern, which is a new approach for face recognition using symmetry, has been presented. The method has three main stages, preprocessing, feature extraction, and classification. A Two-Dimensional Discrete Wavelet Transform with Single-Level and Gaussian Low-Pass Filter were used, separately, for preprocessing. The Local Binary Pattern, Gray Level Co-Occurrence Matrix, and the Gabor filter were used for feature extraction, and the Euclidean Distance was used for classification. The proposed method was implemented and evaluated using the Olivetti Research Laboratory (ORL) and Yale datasets. This study also examined the importance of the preprocessing stage in a face recognition system. The experimental results showed that the proposed method had a recognition accuracy of 100%, for both the ORL and Yale datasets, and these recognition rates were higher than the methods in the literature.

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“…For fingerprint recognition, voice recognition, and iris recognition and other features, face recognition is more rapid, vivid, and natural, and the face image can be obtained without interfering with the subject's face [5]. For the face of the recognized person, no other restrictions are required, and the face recognition system does not need a dedicated image collection device, so the cost is also low, and the face recognition technology has gradually been favored by people [6,7]. At present, many researchers in China and other countries have carried out in-depth research on face recognition technology, among which the representative research institutions are the MIT Media Lab and the Artificial Intelligence Laboratory.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Accuracy Rate of Distributed Virtual Reality Face Recognition Based on Personal Intelligent Terminal

2022

Mobile Information Systems

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Due to the fact that problem occurs to the traditional algorithm in the internal change process of the sample set, this paper proposes a distributed virtual reality face recognition algorithm based on the personal intelligent terminal. By the utilization of the face image library with multiple description functions through expansion, the pixel intensity of the face image has data information, the original face image can be used to generate a face image with higher intensity pixels, and the mirror image can increase the detailed data information of the image. By effectively combining source domain images with initialization images, a scalable image set can be generated. According to parameter-free modeling, the image can be extended as a personal intelligent terminal. The test sample images of the same category and different source domains can form an image set and finally use the residual discriminant function to complete the face recognition. Finally, the results of the experimental analysis show that the distributed virtual reality face recognition accuracy analysis method proposed in this paper can not only build a face image database with multiple reconstruction functions but also effectively use the correlation within the face samples to improve the accuracy of face recognition and implement face recognition. Compared with other facial recognition algorithms, this algorithm has higher recognition accuracy and faster recognition speed.

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Improving the Face Recognition Accuracy under Varying Illumination Conditions for Local Binary Patterns and Local Ternary Patterns Based on Weber-Face and Singular Value Decomposition

Cited by 11 publications

References 17 publications

[Retracted] Optimum Feature Selection with Particle Swarm Optimization to Face Recognition System Using Gabor Wavelet Transform and Deep Learning

[Retracted] Optimum Feature Selection with Particle Swarm Optimization to Face Recognition System Using Gabor Wavelet Transform and Deep Learning

Face Recognition with Symmetrical Face Training Samples Based on Local Binary Patterns and the Gabor Filter

Analysis of Accuracy Rate of Distributed Virtual Reality Face Recognition Based on Personal Intelligent Terminal

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