Emotion Recognition from Facial Images using Hybrid Deep Learning Models

Yaseen, Arfa Fatima; Shaukat, Arslan; Alam, Maria

doi:10.1109/icodt255437.2022.9787474

Cited by 6 publications

(3 citation statements)

References 38 publications

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“…The suggested technique is tested using two reference data sets, on FER2013 and JAFFE datasets, respectively, using a hybrid model of Efficient-NetB0+VGG16 [5]. The purpose of this paper is an efficient deep learning technique using a convolutional neural network model for emotion recognition, age detection, and gender detection from facial images [6]. Neuroscience, mental health, behavioral science, and artificial intelligence are just a few of the applications of machine learning.…”

Section: Literature Surveymentioning

confidence: 99%

An Automated System for Depression Detection Based on Facial and Vocal Features

Patil,

Khedkar

2023

IJRITCC

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Diagnosing depression is a challenge due to the subjective nature of traditional tools like questionnaires and interviews. Researchers are exploring alternative methods for detecting depression, such as using facial and vocal features. This study investigated the potential of facial and vocal features for depression detection using two datasets: images of facial expressions with emotion labels, and a vocal expression dataset with positive and negative words. Four deep-learning models were evaluated for depression detection from facial expressions, and two traditional machine-learning models were trained for sentiment analysis on the vocal expression dataset. The CNN model performed best for facial expression analysis, while the Naive Bayes model performed best for vocal expression analysis. The models were integrated into a web application for depression analysis, allowing users to upload a video and receive an analysis of their facial and vocal expressions for signs of depression. This study demonstrates the potential of using facial and vocal features for depression detection and provides insight into the performance of different machine learning algorithms for this task. The web application has the potential to be a useful tool for individuals monitoring their mental health and may support mental health professionals in their clinical assessments of depression.

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Section: Literature Surveymentioning

confidence: 99%

An Automated System for Depression Detection Based on Facial and Vocal Features

Patil,

Khedkar

2023

IJRITCC

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“…It is usual and imitates emotions as well as many mental actions, physical signs, and social contacts. Facial expression recognition (FER) is commonly utilized in many applications such as elderly care, human-computer communication, security monitoring, customer satisfaction detection, the criminal justice system, medical diagnostics, smart card applications as well as enlarged law enforcement services from smart cities [2]. In the current study, vision sensor-based FER has more attention and great potential in actual FER detection [3].…”

Section: Introductionmentioning

confidence: 98%

Bioinspired Image Processing Enabled Facial Emotion Recognition Using Equilibrium Optimizer With a Hybrid Deep Learning Model

Alzahrani

2024

IEEE Access

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Owing to the unpredictable nature of human facial expression, Facial emotion recognition (FER) from facial images becomes a tedious process. FER is a field within artificial intelligence (AI) and computer vision (CV) that concentrates on developing algorithms and technologies to interpret and analyze emotional expressions shown on human faces. The major intention of this area of research is to enable computers and machines to automatically classify and detect emotions, including sadness, happiness, anger, etc., based on facial expressions and cues. Deep learning (DL) systems namely recurrent neural network (RNN) and convolutional neural network (CNN) can be used for the task of automatically classifying and detecting emotions from human facial expressions. Furthermore, Bioinspired Image Processing Enabled FER combines principles from biological systems with image processing techniques to better the robustness and accuracy of FER. This technique is inspired by natural processes to enhance the interpretation and understanding of human emotion conveyed by facial expressions. This study designs a new Bioinspired Image Processing Enabled Facial Emotion Recognition using an Equilibrium Optimizer with Hybrid Deep Learning (BIPFER-EOHDL) model. The primary objectives of the BIPFER-EOHDL technique lie in the effectual and automated identification of facial expressions using a hyperparameter-tuned DL algorithm. In the presented BIPFER-EOHDL technique, the median filtering (MF) approach can be applied for image preprocessing. Besides, the BIPFER-EOHDL method applies the EfficientNetB7 model for the process of feature extraction. Meanwhile, the hyperparameter selection of the EfficientNetB7 model takes place by the use of the EO algorithm. Finally, the multi-head attention bi-directional long short-term memory (MA-BLSTM) model is exploited for the recognition and classification of facial emotions. A wide-ranging simulation analysis was performed to validate the higher FER outcomes of the BIPFER-EOHDL methodology. The simulation results stated that the BIPFER-EOHDL technique accomplishes better FER results than other recent approaches.

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“…It is mainly uses compound scaling and there are many various methods to scale a convolution network in terms of height, width or depth. Compound scaling improves the performance of the network[17]. MobileNet: MobileNet is a deep convolutional neural network model open-sourced by Google…”

mentioning

confidence: 99%

Pre-Trained Deep Learning Models for Detecting Strikeouts in Kannada Handwritten Documents

Bhargav, Shivayogi B. Ullagaddi

2023

tjjpt

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In the digital document analysis, handwritten character recognition is a challenging area. Various methods are proposed in the literature to identify the strike-outs in various languages. Kannada handwritten classification is a crucial machine vision problem due to its various practical applications in the development of recognition systems. Identifying of strikeout string on the given Kannada statement is an important challenge to develop robust recognition system. In the present study, pre-trained models such as DenseNet121, EficientNetB0, MobileNet, InceptionResNetV2 are used to recognize strikeout Kannada words. Experimental analysis indicates that EficientNetB0 performed better for strikeout Kannada words and InceptionResNetV2 results in higher performance for non-strikeout Kannada words.

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Emotion Recognition from Facial Images using Hybrid Deep Learning Models

Cited by 6 publications

References 38 publications

An Automated System for Depression Detection Based on Facial and Vocal Features

An Automated System for Depression Detection Based on Facial and Vocal Features

Bioinspired Image Processing Enabled Facial Emotion Recognition Using Equilibrium Optimizer With a Hybrid Deep Learning Model

Pre-Trained Deep Learning Models for Detecting Strikeouts in Kannada Handwritten Documents

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