A Novel Partitioned Random Forest Method-Based Facial Emotion Recognition
Facial emotion recognition (FER) has been applied to various sectors, including e-learning, marketing, humanoid robot design, HMI/HCI applications, and medicine. The rapid development of intelligent technologies has led researchers to strive to improve facial emotion recognition techniques. A range...
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2025-01-01
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| author | Hanif Heidari M. Murugappan Javeed Shaikh-Mohammed Muhammad E. H. Chowdhury |
| author_facet | Hanif Heidari M. Murugappan Javeed Shaikh-Mohammed Muhammad E. H. Chowdhury |
| author_sort | Hanif Heidari |
| collection | DOAJ |
| description | Facial emotion recognition (FER) has been applied to various sectors, including e-learning, marketing, humanoid robot design, HMI/HCI applications, and medicine. The rapid development of intelligent technologies has led researchers to strive to improve facial emotion recognition techniques. A range of machine learning (ML) methods can be used to recognize facial expressions based on data from small to large datasets. Random Forest (RF) is simpler and more efficient than other ML algorithms. Some modified versions of RF have been developed to improve classification accuracy in the literature. Most improved RF versions modify attribute selection processes or combine them with other machine learning algorithms, increasing their complexity. Identifying an appropriate training dataset and determining its size remain open questions. The partitioned random forests (PRFs) approach is proposed as a modified strategy for improving FER. The proposed method divides multiple regions (different data lengths) into the feature space, allowing the algorithm to learn more complex decision boundaries. Using three statistical measures Lyapunov exponents (LE), Correlation Dimension (CD), and approximate entropy (AE), we evaluated the performance of machine learning algorithms over different data lengths. A crucial role for classification accuracy is played by the Lyapunov exponent or LE and the size of the dataset. A PRF is more effective on smaller datasets and with higher LE values. The proposed method for partitioning the datasets has been successfully tested on the FER dataset to classify six basic emotions (sadness, anger, fear, surprise, disgust, and happiness). Based on our numerical results, PRF performed better than traditional RF and other ML methods for FER, providing 98.37% mean absolute accuracy. Thus, a robust and useful method for improving classification rates is proposed for both small and large datasets. |
| format | Article |
| id | doaj-art-7b349deff3f54c5eab5dc18bbdb5e7ec |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-7b349deff3f54c5eab5dc18bbdb5e7ec2025-08-20T02:14:49ZengIEEEIEEE Access2169-35362025-01-0113750897510310.1109/ACCESS.2025.356036210964216A Novel Partitioned Random Forest Method-Based Facial Emotion RecognitionHanif Heidari0https://orcid.org/0000-0002-6321-3295M. Murugappan1https://orcid.org/0000-0002-5839-4589Javeed Shaikh-Mohammed2https://orcid.org/0000-0001-9079-3101Muhammad E. H. Chowdhury3https://orcid.org/0000-0003-0744-8206Department of Applied Mathematics, Damghan University, Damghan, IranDepartment of Electronics and Communication Engineering, Intelligent Signal Processing (ISP) Research Laboratory, Kuwait College of Science and Technology, Doha, KuwaitDepartment of Biomedical Technology, College of Applied Medical Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi ArabiaDepartment of Electrical Engineering, Qatar University, Doha, QatarFacial emotion recognition (FER) has been applied to various sectors, including e-learning, marketing, humanoid robot design, HMI/HCI applications, and medicine. The rapid development of intelligent technologies has led researchers to strive to improve facial emotion recognition techniques. A range of machine learning (ML) methods can be used to recognize facial expressions based on data from small to large datasets. Random Forest (RF) is simpler and more efficient than other ML algorithms. Some modified versions of RF have been developed to improve classification accuracy in the literature. Most improved RF versions modify attribute selection processes or combine them with other machine learning algorithms, increasing their complexity. Identifying an appropriate training dataset and determining its size remain open questions. The partitioned random forests (PRFs) approach is proposed as a modified strategy for improving FER. The proposed method divides multiple regions (different data lengths) into the feature space, allowing the algorithm to learn more complex decision boundaries. Using three statistical measures Lyapunov exponents (LE), Correlation Dimension (CD), and approximate entropy (AE), we evaluated the performance of machine learning algorithms over different data lengths. A crucial role for classification accuracy is played by the Lyapunov exponent or LE and the size of the dataset. A PRF is more effective on smaller datasets and with higher LE values. The proposed method for partitioning the datasets has been successfully tested on the FER dataset to classify six basic emotions (sadness, anger, fear, surprise, disgust, and happiness). Based on our numerical results, PRF performed better than traditional RF and other ML methods for FER, providing 98.37% mean absolute accuracy. Thus, a robust and useful method for improving classification rates is proposed for both small and large datasets.https://ieeexplore.ieee.org/document/10964216/Partitioned random forestrandom forestmachine learningfacial emotion recognitionclassificationemotions |
| spellingShingle | Hanif Heidari M. Murugappan Javeed Shaikh-Mohammed Muhammad E. H. Chowdhury A Novel Partitioned Random Forest Method-Based Facial Emotion Recognition IEEE Access Partitioned random forest random forest machine learning facial emotion recognition classification emotions |
| title | A Novel Partitioned Random Forest Method-Based Facial Emotion Recognition |
| title_full | A Novel Partitioned Random Forest Method-Based Facial Emotion Recognition |
| title_fullStr | A Novel Partitioned Random Forest Method-Based Facial Emotion Recognition |
| title_full_unstemmed | A Novel Partitioned Random Forest Method-Based Facial Emotion Recognition |
| title_short | A Novel Partitioned Random Forest Method-Based Facial Emotion Recognition |
| title_sort | novel partitioned random forest method based facial emotion recognition |
| topic | Partitioned random forest random forest machine learning facial emotion recognition classification emotions |
| url | https://ieeexplore.ieee.org/document/10964216/ |
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