Facial emotion recognition using modified HOG and LBP features with deep stacked autoencoders

Lakshmi, D. Rajya; Ponnusamy, R.

doi:10.1016/j.micpro.2021.103834

“…From the existing literature, it has been found that the Decision tree (DT) [12,13], Support Vector Machine (SVM) [14,15], Random Forest (RF) [16,17], and Artificial Neural Network (ANN) [9] are commonly utilized to recognize the human emotions. Jain et al (2019) [18] [21] implemented a modified histogram of oriented gradients (HOG) and local binary pattern (LBP), i.e., HOGLBP to extract the features. Although these methods achieve good performance, but suffer from the overfitting issue.…”

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Efficient Facial Emotion Recognition Model using Deep Convolutional Neural Network and Modified Joint Trilateral Filter

Kumari

¹

,

Bhatia

²

2021

Preprint

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Facial emotion recognition extracts the human emotions from the images and videos. As such, it requires an algorithm to understand and model the relationships between faces and facial expressions, and to recognize human emotions. Recently, deep learning models are extensively utilized enhance the facial emotion recognition rate. However, the deep learning models suffer from the overfitting issue. Moreover, deep learning models perform poorly for images which have poor visibility and noise. Therefore, in this paper, a novel deep learning based facial emotion recognition tool is proposed. Initially, a joint trilateral filter is applied to the obtained dataset to remove the noise. Thereafter, contrast-limited adaptive histogram equalization (CLAHE) is applied to the filtered images to improve the visibility of images. Finally, a deep convolutional neural network is trained. Nadam optimizer is also utilized to optimize the cost function of deep convolutional neural networks. Experiments are achieved by using the benchmark dataset and competitive human emotion recognition models. Comparative analysis demonstrates that the proposed facial emotion recognition model performs considerably better compared to the competitive models.

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“…This helped to improve the robustness and generalization ability of the recognition models. Lakshmi et al [21] proposed hybrid model to recognize facial emotions. In this, face regions were selected through Viola-Jones method.…”

Section: Related Workmentioning

confidence: 99%

“…From the existing literature, it has been found that the Decision tree (DT) [12,13], Support Vector Machine (SVM) [14,15], Random Forest (RF) [16,17], and Artificial Neural Network (ANN) [9] [21] implemented a modified histogram of oriented gradients (HOG) and local binary pattern (LBP), i.e., HOGLBP to extract the features. Although these methods achieve good performance, but suffer from the overfitting issue.…”

Section: Introductionmentioning

confidence: 99%

Efficient facial emotion recognition model using deep convolutional neural network and modified joint trilateral filter

Kumari

¹

,

Bhatia

²

2022

View full text Add to dashboard Cite

Facial emotion recognition extracts the human emotions from the images and videos. As such, it requires an algorithm to understand and model the relationships between faces and facial expressions, and to recognize human emotions. Recently, deep learning models are extensively utilized enhance the facial emotion recognition rate. However, the deep learning models suffer from the overfitting issue. Moreover, deep learning models perform poorly for images which have poor visibility and noise. Therefore, in this paper, a novel deep learning based facial emotion recognition tool is proposed. Initially, a joint trilateral filter is applied to the obtained dataset to remove the noise. Thereafter, contrast-limited adaptive histogram equalization (CLAHE) is applied to the filtered images to improve the visibility of images. Finally, a deep convolutional neural network is trained. Nadam optimizer is also utilized to optimize the cost function of deep convolutional neural networks. Experiments are achieved by using the benchmark dataset and competitive human emotion recognition models. Comparative analysis demonstrates that the proposed facial emotion recognition model performs considerably better compared to the competitive models.

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“…HOG is a very powerful descriptor proposed by Dalal and Triggs in 2005, which was initially developed for human detection [73]. However, later it is extended and applied to other topics of computer vision problems including facial recognition [74], gender and age estimation [75], detection of plant pathology's [76] and recognition of facial expressions [77]. HOG describes the Gradient values (G h , G y ) are computed for each pixel using a centered 1 − D derivative filter, in the horizontal and vertical directions.…”

Section: Histogram Of Oriented Gradient (Hog)mentioning

confidence: 99%

An Intelligent Feature Selection using Archimedes Optimization algorithm for Facial Analysis

Neggaz

¹

,

Fizazi

²

2021

Preprint

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Human facial analysis (HFA) has recently become an attractive topic for computer vision research due to the technological progress and the increase of mobile applications. HFA explores several issues as gender recognition, facial expression, age, and race recognition for automatically understanding social life. In addition, the development of several algorithms inspired by swarm intelligence, biological inspiration, and physical/mathematical rules allow giving another dimension of feature selection in the field of machine learning and computer vision. This paper develops a novel wrapper feature selection method for gender recognition using the Archimedes optimization algorithm (AOA). The paper's primary purpose is to automatically determine the optimal face area using AOA to recognize the gender of a human person categorized by two classes (Men and women). In this paper, the facial image is divided into several sub-regions (blocks), where each area provides a vector of characteristics using one method from handcrafted techniques as the local binary pattern (LBP), histogram oriented gradient (HOG), or Grey level co-occurrence matrix (GLCM). The proposed method (AOA) is assessed on two publicly datasets: Georgia Tech Face dataset (GT) and the Brazilian FEI dataset. The experimental results show a good performance of AOA compared to other recent and competitive optimizers as Sine cosine algorithm (SCA), Henry Gas Solubility Optimization (HGSO), Equilibrium Optimizer (EO), Emperor Penguin Optimizer (EPO), Harris Hawks Optimize (HHO), Multi-verse Optimizer (MVO) and Manta-ray Foraging Optimizer (MRFO) in terms of accuracy and the number of the selected area.

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Facial emotion recognition using modified HOG and LBP features with deep stacked autoencoders

Cited by 65 publications

References 18 publications

WITHDRAWN: Efficient Facial Emotion Recognition Model using Deep Convolutional Neural Network and Modified Joint Trilateral Filter

WITHDRAWN: Efficient Facial Emotion Recognition Model using Deep Convolutional Neural Network and Modified Joint Trilateral Filter

Efficient facial emotion recognition model using deep convolutional neural network and modified joint trilateral filter

An Intelligent Feature Selection using Archimedes Optimization algorithm for Facial Analysis

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