A scheme of features fusion for facial expression analysis: A facial action recognition

Kumar, Naresh; Bhargava, Deepshikha

doi:10.1080/09720510.2017.1395189

Cited by 15 publications

(6 citation statements)

References 18 publications

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“…• Finally, the 8-bit binary number, beginning from the upper left neighbour, is considered as a decimal number going somewhere in the range of 0 and 255 [16,17]. The operator relegates a name to each pixel of an image by thresholding the 3 x 3 neighbourhood of every pixel with the middle pixel esteem.…”

Section: Figure 6 Facial Landmark Pointsmentioning

confidence: 99%

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

Kulkarni¹,

Rajesh²

2021

RIA

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Facial expression recognition assumes a significant function in imparting the feelings and expectations of people. Recognizing facial emotions in an uncontrolled climate is more problematic than in a controlled climate due to progress in hindrance, glare and clamor. This paper, we demonstrate another system for successful facial emotion recognition from ongoing face images. Dissimilar to different strategies which invest a lot of energy by partitioning the picture into squares or entire face pictures; our strategy extricates the discriminative component from notable face areas and afterward consolidates with surface and direction highlights for better portrayal. We also made sub-categories in the main expressions like happy and sad, to identify the level of happiness and sadness and to check whether the person is really happy/sad or acting to be happy/sad. Moreover, we lessen the information measurement by choosing the profoundly discriminative highlights. The proposed system is fit for giving high matching precision rate even within the sight of impediments, light, and commotion. To show the heartiness of the expected structure, it utilized two freely accessible testing dataset. These trial results show that the presentation of the expected structure is superior to current strategies, which demonstrate the impressive capability of consolidating mathematical highlights with appearance-based highlights.

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Section: Figure 6 Facial Landmark Pointsmentioning

confidence: 99%

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

Kulkarni¹,

Rajesh²

2021

RIA

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“…Gabor wavelet transform, 12 Haar wavelet transform, 13 active presence model, 14 and local binary pattern (LBP) are feature extraction techniques applied on static images, whereas dynamic-based 15 – 17 methods adopt the temporal relationship in the sequence of input facial expressions within adjacent frames. The hidden Markov model, support vector machine (SVM), 17 artificial neural networks, 18 and AdaBoost 18 are widely used techniques for facial expression detection.…”

Section: Introductionmentioning

confidence: 99%

Real-time emotion recognition using end-to-end attention-based fusion network

et al. 2023

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.Real-time emotion detection based on facial expression is an innovative research field that has been applied in several areas, such as health, human–machine vision, and autonomous safety. Researchers in object detection are involved in developing methods to interpret, code facial expressions, and extract these features to be better predicted by machines. Furthermore, the success of deep learning with different architectures is exploited to achieve better performance. But these methods drastically fail in excessive sweating in different health conditions. We aim to create a dataset in different health conditions and detect facial emotion using the encoder and decoder-based deep learning methodology. The proposed architecture and the dataset present the progress made by comparing the other proposed methods and the quantitative and qualitative results obtained. The major benefit of our study is to enhance the emotion detection efficiency with other proposed methods and real-time applications for different health conditions. We propose the application of feature extraction of facial expressions with an end-to-end attention module-based fusion network for detecting different facial emotions (happy, angry, neutral, surprised, etc.) with an accuracy of 99.68%. The proposed system depends upon the human face; as we know, the face reflects human brain activities or emotions.

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“…Gabor wavelet transform 3 , Haar wavelet transform 4 , Local Binary Pattern (LBP), and Active Presence Models (AAM) 5 are the feature extraction methods based on static images. Whereas dynamic-based [6][7][8] approaches assume the temporal association in the sequence of input facial expression within clinging frames.…”

Section: Introductionmentioning

confidence: 99%

Four-Layer ConvNet to Facial Emotion Recognition with Minimal Epochs and the Significance of Data Diversity

Debnath¹,

Reza²,

Rahman³

et al. 2021

Preprint

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Emotion recognition defined as identifying human emotion and is directly related to different fields such as human-computer interfaces, human emotional processing, irrational analysis, medical diagnostics, data-driven animation, human-robot communi- cation and many more. The purpose of this study is to propose a new facial emotional recognition model using convolutional neural network. Our proposed model, “ConvNet”, detects seven specific emotions from image data including anger, disgust, fear, happiness, neutrality, sadness, and surprise. This research focuses on the model’s training accuracy in a short number of epoch which the authors can develop a real-time schema that can easily fit the model and sense emotions. Furthermore, this work focuses on the mental or emotional stuff of a man or woman using the behavioral aspects. To complete the training of the CNN network model, we use the FER2013 databases, and we test the system’s success by identifying facial expressions in the real-time. ConvNet consists of four layers of convolution together with two fully connected layers. The experimental results show that the ConvNet is able to achieve 96% training accuracy which is much better than current existing models. ConvNet also achieved validation accuracy of 65% to 70% (considering different datasets used for experiments), resulting in a higher classification accuracy compared to other existing models. We also made all the materials publicly accessible for the research community at: https://github.com/Tanoy004/Emotion-recognition-through-CNN.

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A scheme of features fusion for facial expression analysis: A facial action recognition

Cited by 15 publications

References 18 publications

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

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

Real-time emotion recognition using end-to-end attention-based fusion network

Four-Layer ConvNet to Facial Emotion Recognition with Minimal Epochs and the Significance of Data Diversity

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