On Constrained Local Model Feature Normalization for Facial Expression Recognition

Pan, Zhenglin; Polceanu, Mihai; Lisetti, Christine L.

doi:10.1007/978-3-319-47665-0_35

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…LBP [5,16] has its roots in 2D texture analysis. LBP will summarize the local pattern of an image by comparing each pixel with its neighboring pixels.…”

Section: Local Binary Patternsmentioning

confidence: 99%

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A Facial Expression Recognition Model using Support Vector Machines

Bellamkonda¹,

Gopalan²

2018

IJMSC

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Facial Expression Recognition (FER) has gained interest among researchers due to its inevitable role in the human computer interaction. In this paper, an FER model is proposed using principal component analysis (PCA) as the dimensionality reduction technique, Gabor wavelets and Local binary pattern (LBP) as the feature extraction techniques and support vector machine (SVM) as the classification technique. The experimentation was done on Cohn-Kanade, JAFFE, MMI Facial Expression datasets and real time facial expressions using a webcam. The proposed methods outperform the existing methods surveyed.

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“…LBP [5,16] has its roots in 2D texture analysis. LBP will summarize the local pattern of an image by comparing each pixel with its neighboring pixels.…”

Section: Local Binary Patternsmentioning

confidence: 99%

“…For the development of intelligent robotics, critical health care, student satisfaction and other application areas, emotional interaction between machine and human became the fundamental basis. So, researchers are intensely trying to improve the accuracy [15][16][17][18] of the FER systems.…”

Section: Introductionmentioning

confidence: 99%

A Facial Expression Recognition Model using Support Vector Machines

Bellamkonda¹,

Gopalan²

2018

IJMSC

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“…This kind of ability needs to be given to the robots to make the human-robot interaction to be much humanistic than mechanical. There are a good number of feature extraction, and classification studies are available in literature [3][4][5][6][7][8][9][10][11][12] and SVM appears to be a popular classifier for FER systems although Neural Networks [13][14][15][16], Hidden Markov Models [17,18] and KNNs [19,20] have also extensively used in similar such studies.…”

Section: Introductionmentioning

confidence: 99%

Pattern Averaging Technique for Facial Expression Recognition Using Support Vector Machines

Gopalan¹,

Bellamkonda²

2018

IJIGSP

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Facial expression is one of the nonverbal communication methods of identifying an emotional state of a human being. Due to its crucial importance in Human-Robot interaction, facial expression recognition (FER) is in the limelight of recent research activities. Most of the studies consider the whole expression images in their analysis, and it has several has several drawbacks concerning illumination, orientation, texture, zoom level, time and space complexity. In this paper, a novel feature extraction technique called the pattern averaging is studied on whole image data using reduction in the dimension of the image by averaging the neighboring pixels. The study is found to give better results on standard datasets using support vector machine classifier.

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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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On Constrained Local Model Feature Normalization for Facial Expression Recognition

Cited by 6 publications

References 8 publications

A Facial Expression Recognition Model using Support Vector Machines

A Facial Expression Recognition Model using Support Vector Machines

Pattern Averaging Technique for Facial Expression Recognition Using Support Vector Machines

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

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