Facial Expressions Recognition Based on Cognition and Mapped Binary Patterns

Chen, Qi; Li, Min; Wang, Qiushi; Zhang, Huiquan; Xing, Jinling; Gao, Zhifan; Zhang, Huailing

doi:10.1109/access.2018.2816044

Cited by 59 publications

(24 citation statements)

References 32 publications

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“…Appearance methods such as scale invariant feature transform (SIFT), Gabor appearance, local phase quantization can detect the multi-scale, multi-direction of the local texture changes on either specific regions or the whole face to encode the texture [3-4, 8-9, 16]. In [7], mapped local binary pattern with four neighborhoods is used to describe the change of local texture features and then face is divided six regions such as forehead, eyes, nose, mouth, left cheek and right cheek using pseudo 3D model. The paper [8] described the texture feature using angled local directional pattern considering the center pixel.…”

Section: Apperance Featuresmentioning

confidence: 99%

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Feature extraction and classification methods of facial expression: a survey

Htay

2021

Comput. Sci. Inf. Technol.

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Facial Expression is a significant role in affective computing and one of the non-verbal communication for human computer interaction. Automatic recognition of human affects has become more challenging and interesting problem in recent years. Facial Expression is the significant features to recognize the human emotion in human daily life. Facial Expression Recognition System (FERS) can be developed for the application of human affect analysis, health care assessment, distance learning, driver fatigue detection and human computer interaction. Basically, there are three main components to recognize the human facial expression. They are face or face’s components detection, feature extraction of face image, classification of expression. The study proposed the methods of feature extraction and classification for FER.

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Section: Apperance Featuresmentioning

confidence: 99%

“…The studies in references [3,[5][6][7] classified six universal emotions as happiness, angry, sadness, surprise, fear, and disgust. In [9, 13,15,[23][24] have classified one more class as neutral and [8,17,23] have done contempt class.…”

Section: Introductionmentioning

confidence: 99%

Feature extraction and classification methods of facial expression: a survey

Htay

2021

Comput. Sci. Inf. Technol.

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“…So far, the recognition of distinct facial expression is commonly based on six fundamental emotions or its subsets: happiness, fear, sadness, anger, surprise, disgust. Qi C. et al [8] have proposed a paper on Recognition of facial expression based on binary pattern and cognition. In texture feature extraction local binary pattern has an advantage.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised facial expression detection using genetic algorithm

Dembani¹,

Wang²,

Sun³

et al. 2020

NNW

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Interpersonal communication can be done by understanding the clues of facial expressions. As its importance increase in behavior and clinical studies, so automatic detection of facial expressions is an open research area for the last few decades. Efforts of expression detection by a human being are easy and effective but the machine needs some more understanding. This paper proposes a face expression clustering using a genetic algorithm. Image get convert into binary format for finding the related cluster selection in different phases of genetic algorithm. Proposed work has utilized a modified teacher learning-based optimization algorithm where the population gets updated in each phase to get the best representative features. A real dataset of facial expression was used in this work. A comparison of the proposed model was done with existing models on different evaluation parameters. It was obtained that the proposed work has improved precision, recall, the accuracy of facial expression identification without any training.

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“…C. Qi et al [37] suggested a novel expression recognition technique is obtainable built on cognition plus mapped binary patterns. This method is built on the LBP operator to excerpt the facial outlines and further the formation of pseudo-3-D method is used to fragment the face space into six facial expression sub-spaces.…”

Section: Related Workmentioning

confidence: 99%

Face Recognition Approaches: A Survey

Reddy¹,

Krishna²,

Sumalatha³

2018

IJET

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Face Recognition (FR) is a significant area in computer vision plus pattern recognition. The face is the easiest mode to discriminate the specific individuality of every other. FR is a particular identification scheme that usages particular features of an individual to recognize the individual's identity. The challenges in FR are aged, facial terms, variations in the imaging surroundings, illumination plus posture of the face. Specially, in this study firstly we mark an outline of FR that includes definition, types and problems. Secondly, we provided a complete related work of FR. The objective of this study is to provide a comprehensive outline on the work that has been carried out over the previous spans in the progressing area of FR. This study offers an extensive view of theories, methodologies, up-to-date techniques for FR.

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Facial Expressions Recognition Based on Cognition and Mapped Binary Patterns

Cited by 59 publications

References 32 publications

Feature extraction and classification methods of facial expression: a survey

Feature extraction and classification methods of facial expression: a survey

Unsupervised facial expression detection using genetic algorithm

Face Recognition Approaches: A Survey

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