A Survey On Face Recognition Algorithms

Alghamdi, Jana; Alharthi, Renad; Alghamdi, Raneem; Alsubaie, Wafaa; Alsubaie, Rawan; Alqahtani, Dana; ramadan, Rawan Al; Alqarni, Leena; Alshammari, Reem

doi:10.1109/iccais48893.2020.9096726

Cited by 14 publications

(8 citation statements)

References 8 publications

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“…It is a prevalent technique for real-time object detection developed by Paul Viola and Michael Jones [ 66 ]. There are two stages involved in this algorithm: training and detection.…”

Section: Proposed System Methodologymentioning

confidence: 99%

Deep Neural Network Approach for Pose, Illumination, and Occlusion Invariant Driver Emotion Detection

Sukhavasi

Elleithy

El-Sayed

et al. 2022

IJERPH

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Monitoring drivers’ emotions is the key aspect of designing advanced driver assistance systems (ADAS) in intelligent vehicles. To ensure safety and track the possibility of vehicles’ road accidents, emotional monitoring will play a key role in justifying the mental status of the driver while driving the vehicle. However, the pose variations, illumination conditions, and occlusions are the factors that affect the detection of driver emotions from proper monitoring. To overcome these challenges, two novel approaches using machine learning methods and deep neural networks are proposed to monitor various drivers’ expressions in different pose variations, illuminations, and occlusions. We obtained the remarkable accuracy of 93.41%, 83.68%, 98.47%, and 98.18% for CK+, FER 2013, KDEF, and KMU-FED datasets, respectively, for the first approach and improved accuracy of 96.15%, 84.58%, 99.18%, and 99.09% for CK+, FER 2013, KDEF, and KMU-FED datasets respectively in the second approach, compared to the existing state-of-the-art methods.

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“…It is a prevalent technique for real-time object detection developed by Paul Viola and Michael Jones [ 66 ]. There are two stages involved in this algorithm: training and detection.…”

Section: Proposed System Methodologymentioning

confidence: 99%

Deep Neural Network Approach for Pose, Illumination, and Occlusion Invariant Driver Emotion Detection

Sukhavasi

Elleithy

El-Sayed

et al. 2022

IJERPH

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“…Alghamdi et al gave a comparison between various technologies that can be used for facial recognition. Types of detection algorithms like SVM, the Viola-Jones (VJ) algorithm, the Kanade Lucas Tomasi algorithm, the AdaBoost algorithm, the hybrid face detection algorithm, and the Elman Neural Network have been introduced [24]. Li et al discussed the future development direction and potential application prospects of FER.…”

Section: Related Workmentioning

confidence: 99%

Multimodal emotion recognition based on the fusion of vision, EEG, ECG, and EMG signals

Bhatlawande,

Shilaskar,

Pramanik

et al. 2024

Int. j. electr. comput. eng. syst. (Online)

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This paper presents a novel approach for emotion recognition (ER) based on Electroencephalogram (EEG), Electromyogram (EMG), Electrocardiogram (ECG), and computer vision. The proposed system includes two different models for physiological signals and facial expressions deployed in a real-time embedded system. A custom dataset for EEG, ECG, EMG, and facial expression was collected from 10 participants using an Affective Video Response System. Time, frequency, and wavelet domain-specific features were extracted and optimized, based on their Visualizations from Exploratory Data Analysis (EDA) and Principal Component Analysis (PCA). Local Binary Patterns (LBP), Local Ternary Patterns (LTP), Histogram of Oriented Gradients (HOG), and Gabor descriptors were used for differentiating facial emotions. Classification models, namely decision tree, random forest, and optimized variants thereof, were trained using these features. The optimized Random Forest model achieved an accuracy of 84%, while the optimized Decision Tree achieved 76% for the physiological signal-based model. The facial emotion recognition (FER) model attained an accuracy of 84.6%, 74.3%, 67%, and 64.5% using K-Nearest Neighbors (KNN), Random Forest, Decision Tree, and XGBoost, respectively. Performance metrics, including Area Under Curve (AUC), F1 score, and Receiver Operating Characteristic Curve (ROC), were computed to evaluate the models. The outcome of both results, i.e., the fusion of bio-signals and facial emotion analysis, is given to a voting classifier to get the final emotion. A comprehensive report is generated using the Generative Pretrained Transformer (GPT) language model based on the resultant emotion, achieving an accuracy of 87.5%. The model was implemented and deployed on a Jetson Nano. The results show its relevance to ER. It has applications in enhancing prosthetic systems and other medical fields such as psychological therapy, rehabilitation, assisting individuals with neurological disorders, mental health monitoring, and biometric security.

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“…In [13], The paper explores the concept of Facial Recognition Systems, a technology utilizing algorithms to identify and verify human faces in digital images. The survey aims to compare various algorithms employed in facial recognition, an important aspect of computer vision with applications in security and other sectors.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Approach to Smart Mirror Technology for Enhanced Elderly Health Monitoring

2023

IJSREM

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Abstract—The integration of smart mirror technology for health monitoring in elderly populations discusses the benefits and challenges of using smart mirrors as a proactive healthcare solution. The study emphasizes features such as facial recognition, posture analysis, and skin condition detection, enabling real-time data collection and personalized recommendations for preventive care. Usability, privacy concerns, and integration issues are considered. Through a review of relevant research, the paper highlights the potential of smart mirrors for proactive health monitoring in the elderly. It offers insights to researchers and healthcare professionals seeking innovative solutions for personalized care. Keywords—Smart mirror, smart assistant, health issues, elderly care, human healthcare, IoT, emotion recognition, mental monitoring

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A Survey On Face Recognition Algorithms

Cited by 14 publications

References 8 publications

Deep Neural Network Approach for Pose, Illumination, and Occlusion Invariant Driver Emotion Detection

Deep Neural Network Approach for Pose, Illumination, and Occlusion Invariant Driver Emotion Detection

Multimodal emotion recognition based on the fusion of vision, EEG, ECG, and EMG signals

A Novel Approach to Smart Mirror Technology for Enhanced Elderly Health Monitoring

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