Video Based Sub-Categorized Facial Emotion Detection Using LBP and Edge Computing

Kulkarni, Praveen; Rajesh, T.

doi:10.18280/ria.350106

Cited by 7 publications

(7 citation statements)

References 20 publications

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“…Kulkarni et al 40 proposed an approach for facial emotion identification from continuing face images. For a more accurate representation, the approach isolates the discriminative component from prominent facial areas before combining it with surface and direction highlights.…”

Section: Video Sequence-based Approachmentioning

confidence: 99%

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Comprehensive review and analysis on facial emotion recognition methods

Vats,

Kumar,

Rai

et al. 2023

J. Electron. Imag.

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Facial emotion recognition (FER) is a significant subject in computer vision and artificial intelligence because of its tremendous academic and commercial potential, such as in cognitive science, health care, virtual reality, and video conferencing in various domains. While FER could be carried out using multiple sensors, this review includes research that exclusively uses face images, since facial expressions are among the key pieces of knowledge in human interactions. We give a brief review of FER research carried out in recent years. We divided the techniques of FER mainly into two parts, i.e., based on the type of approach and based on the type of frame, which is further subdivided into two sub-parts of each classification for more detailed division. First, it explains traditional FER approaches together with a description of the representative classes of FER systems. Deep-learning FER strategies are then addressed using deep networks that allow "end-to-end" learning. This review is also directed toward an up-to-date deep learning strategy, which is a trending topic nowadays. A brief overview of publicly accessible assessment metrics is provided in the later part of this paper, as well as a comparison with the baseline results is presented, which is a norm for quantitative analysis of FER research. The whole analysis also could act as a concise field guide for beginners in the FER sector, providing general details and a common understanding of both the recent state-of-the-art research and established researchers searching for fruitful areas for further research.

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Section: Video Sequence-based Approachmentioning

confidence: 99%

“…Kulkarni et al 40 . proposed an approach for facial emotion identification from continuing face images.…”

Section: Face Emotion Detection Techniquesmentioning

confidence: 99%

Comprehensive review and analysis on facial emotion recognition methods

Vats,

Kumar,

Rai

et al. 2023

J. Electron. Imag.

View full text Add to dashboard Cite

show abstract

“…In prior work, a throw analysis was performed using well-known and widely utilized algorithms (Kulkarni et al, 2020), and many studies focused just on emotion recognition rather than grading. Previous work on grading emotion using a combination of LBP and the KNN algorithm (Kulkarni et al, 2021) yielded findings that were not promising, with an accuracy of only 79 percent. This has prompted us to develop the methods proposed in this paper.…”

Section: Introductionmentioning

confidence: 95%

A Multi-Model Framework for Grading of Human Emotion Using CNN and Computer Vision

Kulkarni

Rajesh

2021

International Journal of Computer Vision and Image Processing

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Emotion analysis is an area which is been widely used in the forensic crime detection domain, a mentoring device for depressed students, psychologically affected patient treatment. The current system helps only in identifying the emotions but not in identifying the level of emotions like whether the individual is truly happy/sad or pretending to be happy /sad. In this proposed work a novel methodology has been introduced. We have rebuilt the Traditional Local Binary Pattern (LBP) feature operator to image the expression and combine the abstract characteristics of facial expression learned from the neural network of deep convolution with the modified features of the texture of the LBP facial expression in the full connection layer. These extracted features have been subjected as input for CNN Alex Net to classify the level of emotions. The results obtained in this phase are used in the confusion matrix for analysis of grading of emotions like Grade-1, Grade-2, and Grade-3 obtained an accuracy of 87.58% in the comparative analysis.

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“…Most emotion recognition research mainly uses visual methods [6,7] that can provide a wealth of information and intuitive performance. But image or video processing technology requires a large number of memory resources on highspecification computers.…”

Section: Introductionmentioning

confidence: 99%

Attention‐based sensor fusion for emotion recognition from human motion by combining convolutional neural network and weighted kernel support vector machine and using inertial measurement unit signals

Zhao

Guo

Sun

et al. 2023

IET Signal Processing

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The remarkable development of human–computer interactions has created an urgent need for machines to be able to recognise human emotions. Human motions play a key role in emphasising and conveying emotions to meet the complexity of daily application scenarios, such as medical rehabilitation and social education. Therefore, this paper aims to explore hidden emotional states from human motions. Accordingly, we proposed a novel approach for emotion recognition using multiple inertial measurement unit (IMU) sensors worn on different body parts. First, the mapping relationship between emotion and human motion was established through fuzzy comprehensive evaluation, and data were collected for six emotional states: sleepy, bored, excited, tense, angry, and distressed. Second, the preprocessed data were used as input in a lightweight convolutional neural network to extract discriminative features. Third, an attention‐based sensor fusion module was developed to obtain the importance scores of each IMU sensor for generating a fused feature representation. In the recognition phase, we constructed a weighted kernel support vector machine (SVM) model with an auxiliary fuzzy function to improve the weight calculation method of kernel functions in a multiple kernel SVM. Finally, the results obtained are compared with those of similar state‐of‐the‐art studies, the proposed method showed a higher accuracy (99.02%) for the six emotional states mentioned above. These findings may promote the development of social robots with non‐verbal emotional communication capabilities.

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Video Based Sub-Categorized Facial Emotion Detection Using LBP and Edge Computing

Cited by 7 publications

References 20 publications

Comprehensive review and analysis on facial emotion recognition methods

Comprehensive review and analysis on facial emotion recognition methods

A Multi-Model Framework for Grading of Human Emotion Using CNN and Computer Vision

Attention‐based sensor fusion for emotion recognition from human motion by combining convolutional neural network and weighted kernel support vector machine and using inertial measurement unit signals

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