Feature extraction comparison for facial expression recognition using adaptive extreme learning machine

Wafi, Muhammad; Bachtiar, Fitra A.; Utaminingrum, Fitri

doi:10.11591/ijece.v13i1.pp1113-1122

Cited by 4 publications

(3 citation statements)

References 29 publications

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“…The paper suggests potential improvements by addressing randomly generated input weights in future research. [17] Jaiswal introduced a transfer learning-based method for facial emotion detection, evaluated on JAFFE and FERC-2013 datasets. The proposed model outperformed existing ones, achieving an accuracy of 98.65% on both datasets.…”

Section: Related Workmentioning

confidence: 99%

Comprehensive Exploration of Facial Emotion Recognition using Conventional Machine Learning and Transfer learning Models

Saravanan,

Poonkodi,

Sankar

2024

Preprint

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Facial emotion recognition plays a vital role in enhancing human-computer interaction by allowing machines to perceive and react to human emotions. This paper conducts an in-depth exploration of various methodologies employed for recognizing facial emotions, emphasizing both traditional machine learning techniques and contemporary transfer learning models. We delve into a variety of algorithms such as support vector machines, and sophisticated neural networks like ResNet, EfficientNet, and MobileNet, assessing their efficacy using the standard MUG Facial Expression dataset. These models are tested to discern complex patterns in facial expressions, vital for accurate emotion detection. Our extensive analysis sheds light on the capabilities and constraints of each approach, providing valuable insights that pave the way for further research and practical deployments in this dynamic field. This comprehensive review aims to guide future advancements and enhance the practicality of facial emotion recognition systems.

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Section: Related Workmentioning

confidence: 99%

Comprehensive Exploration of Facial Emotion Recognition using Conventional Machine Learning and Transfer learning Models

Saravanan,

Poonkodi,

Sankar

2024

Preprint

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“…The focus of their work is on building a robust FER model that can handle variations in facial position and identity. Wafi et al (2023) [19], compare different feature extraction methods, namely the gray level co-occurrence matrix, local binary pattern, and facial landmark (FL) techniques, for detecting human facial expressions. They evaluate the effectiveness of these methods using two datasets of facial expressions.…”

Section: Related Workmentioning

confidence: 99%

Facial Expression Recognition Enhancement Using Convolutional Neural Network

2024

IJSER

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Facial expression recognition (FER) technology is quite popular in the fields of computer vision, security monitoring, image classification, and many other related applications. Enhancing the computer's ability to read facial expressions is important for humancomputer interaction, as it enables machines to understand and interact with human emotions. In this paper, a modified approach using neural networks (CNNs) is presented to accurately identify unique facial expressions. using the changes to a commonly used 12-layer CNN model to improve its performance in facial expression recognition (FER). The model is trained using dataset images, which allows it to better infer people's emotions from their facial features. To enhance the accuracy of the system, a preprocessing stage is incorporated. This stage involves several operations for data augmentation, including changing the color in images, such as HSV (Hue, Saturation, Value), YCbCr (Luma, Blue-difference Chroma, Red-difference Chroma), etc., to facilitate better interpretation of color information. Additionally, the preprocessing stage refines facial expression recognition by manipulating facial features and adding extra RGB details, improving the visual information provided by the images. The study specifically focuses on evaluating the effectiveness of this approach using the KDEF database. The KDEF database contains standardized images of facial expressions, making it suitable for assessing the performance of the proposed system. By combining the modified CNN model, training with images, and the preprocessing operations, the system's performance was significantly enhanced. As a result, it achieved recognition rates of up to 95%, indicating a notable improvement in accurately identifying unique facial expressions compared to previous approaches.

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“…Facial emotion recognition proves to be difficult at times, due to insufficient data and bias issues, making it a continually evolving research. [19], [20]. Hence, this research utilizes EMVO feature selection method to further improve the accuracy in facial emotion recognition.…”

Section: Introductionmentioning

confidence: 99%

Facial emotion recognition using enhanced multi-verse optimizer method

Gummula,

Arumugam,

Aranganathan

2024

IJECE

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In recent years, facial emotion recognition has gained significant improvement and attention. This technology utilizes advanced algorithms to analyze facial expressions, enabling computers to detect and interpret human emotions accurately. Its applications span over a wide range of fields, from improving customer service through sentiment analysis, to enhancing mental health support by monitoring emotional states. However, there are several challenges in facial emotion recognition, including variability in individual expressions, cultural differences in emotion display, and privacy concerns related to data collection and usage. Lighting conditions, occlusions, and the need for diverse datasets also impacts accuracy. To solve these issues, an enhanced multi-verse optimizer (EMVO) technique is proposed to improve the efficiency of recognizing emotions. Moreover, EMVO is used to improve the convergence speed, exploration-exploitation balance, solution quality, and the applicability in different types of optimization problems. Two datasets were used to collect the data, namely YouTube and surrey audio-visual expressed emotion (SAVEE) datasets. Then, the classification is done using the convolutional neural networks (CNN) to improve the performance of emotion recognition. When compared to the existing methods shuffled frog leaping algorithm-incremental wrapper-based subset selection (SFLA-IWSS), hierarchical deep neural network (H-DNN) and unique preference learning (UPL), the proposed method achieved better accuracies, measured at 98.65% and 98.76% on the YouTube and SAVEE datasets, respectively.

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Feature extraction comparison for facial expression recognition using adaptive extreme learning machine

Cited by 4 publications

References 29 publications

Comprehensive Exploration of Facial Emotion Recognition using Conventional Machine Learning and Transfer learning Models

Comprehensive Exploration of Facial Emotion Recognition using Conventional Machine Learning and Transfer learning Models

Facial Expression Recognition Enhancement Using Convolutional Neural Network

Facial emotion recognition using enhanced multi-verse optimizer method

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