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

Xu, Zhengyuan; Liu, Yu; Ye, Mingquan; Yu, Hao; Chen, Xun

doi:10.1155/2018/3025264

Cited by 2 publications

(2 citation statements)

References 43 publications

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“…Also, based on a recent study [114] using this method, faces are divided into patches, and then their features are extracted and then used KNN for classification. Gabor features have also been combined with a patch-based extractor to overcome the lack of accuracy on the linear representation of the small sample size [115]. Another study [116] used this combination where the 3D Gabor features and patch method were used.…”

Section: ) Patch-based Featurementioning

confidence: 99%

A Survey of AI-Based Facial Emotion Recognition: Features, ML & DL Techniques, Age-Wise Datasets and Future Directions

et al. 2021

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Facial expressions are mirrors of human thoughts and feelings. It provides a wealth of social cues to the viewer, including the focus of attention, intention, motivation, and emotion. It is regarded as a potent tool of silent communication. Analysis of these expressions gives a significantly more profound insight into human behavior. AI-based Facial Expression Recognition (FER) has become one of the crucial research topics in recent years, with applications in dynamic analysis, pattern recognition, interpersonal interaction, mental health monitoring, and many more. However, with the global push towards online platforms due to the Covid-19 pandemic, there has been a pressing need to innovate and offer a new FER analysis framework with the increasing visual data generated by videos and photographs.Furthermore, the emotion-wise facial expressions of kids, adults, and senior citizens vary, which must also be considered in the FER research. Lots of research work has been done in this area. However, it lacks a comprehensive overview of the literature that showcases the past work done and provides the aligned future directions. In this paper, the authors have provided a comprehensive evaluation of AI-based FER methodologies, including datasets, feature extraction techniques, algorithms, and the recent breakthroughs with their applications in facial expression identification. To the best of the author's knowledge, this is the only review paper stating all aspects of FER for various age brackets and would significantly impact the research community in the coming years.

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Section: ) Patch-based Featurementioning

confidence: 99%

A Survey of AI-Based Facial Emotion Recognition: Features, ML & DL Techniques, Age-Wise Datasets and Future Directions

et al. 2021

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“…The patch-based representation [41], [82], [83] represents the test sample using small patches from the whole image. The patch is a fixed-scale small partition of a whole image.…”

Section: Linear Representation-based Classification With Structuramentioning

confidence: 99%

Linear Representation-Based Methods for Image Classification: A Survey

2020

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In recent years, linear representation-based methods have been widely researched and applied in the image classification field. Generally speaking, there are three steps within linear representation-based classification (LRC) algorithms. The first step is coding, which uses all training samples to represent the test sample in a linear combination. The second step is subspace approximation, where residuals between the test sample and the linear combination of each class are calculated. The third step is classification, which assigns the class label to the minimum class-specific residual. We classify the LRC methods into six categories: 1) linear representation-based classification methods with norm minimizations, 2) linear representationbased classification methods with constraints, 3) linear representation-based classification methods with feature spaces, 4) linear representation-based classification methods with structural information, 5) linear representation with subspace learning, and 6) linear representation in semi-supervised learning and unsupervised learning. The purpose of this paper is to: 1) make an accurate and clear definition of the linear representation-based method, 2) provide a categorization and a comprehensive survey of the existing linear representation-based classification methods for image classification, 3) Summarize the main applications of linear representation-based methods, 4) provide extensive classification results and a discussion of the linear representation-based methods. Furthermore, this paper summarizes specific applications of the linear representation-based methods. Particularly, we performed extensive experiments to compare twelve linear representation-based classification methods on seven image classification datasets.

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

Cited by 2 publications

References 43 publications

A Survey of AI-Based Facial Emotion Recognition: Features, ML & DL Techniques, Age-Wise Datasets and Future Directions

A Survey of AI-Based Facial Emotion Recognition: Features, ML & DL Techniques, Age-Wise Datasets and Future Directions

Linear Representation-Based Methods for Image Classification: A Survey

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