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- Title
Human Emotion Recognition Based on Spatio-Temporal Facial Features Using HOG-HOF and VGG-LSTM.
- Authors
Chouhayebi, Hajar; Mahraz, Mohamed Adnane; Riffi, Jamal; Tairi, Hamid; Alioua, Nawal
- Abstract
Human emotion recognition is crucial in various technological domains, reflecting our growing reliance on technology. Facial expressions play a vital role in conveying and preserving human emotions. While deep learning has been successful in recognizing emotions in video sequences, it struggles to effectively model spatio-temporal interactions and identify salient features, limiting its accuracy. This research paper proposed an innovative algorithm for facial expression recognition which combined a deep learning algorithm and dynamic texture methods. In the initial phase of this study, facial features were extracted using the Visual-Geometry-Group (VGG19) model and input into Long-Short-Term-Memory (LSTM) cells to capture spatio-temporal information. Additionally, the HOG-HOF descriptor was utilized to extract dynamic features from video sequences, capturing changes in facial appearance over time. Combining these models using the Multimodal-Compact-Bilinear (MCB) model resulted in an effective descriptor vector. This vector was then classified using a Support Vector Machine (SVM) classifier, chosen for its simpler interpretability compared to deep learning models. This choice facilitates better understanding of the decision-making process behind emotion classification. In the experimental phase, the fusion method outperformed existing state-of-the-art methods on the eNTERFACE05 database, with an improvement margin of approximately 1%. In summary, the proposed approach exhibited superior accuracy and robust detection capabilities.
- Subjects
EMOTIONS; EMOTION recognition; DEEP learning; MACHINE learning; FEATURE extraction; SUPPORT vector machines; FACIAL muscles
- Publication
Computers (2073-431X), 2024, Vol 13, Issue 4, p101
- ISSN
2073-431X
- Publication type
Article
- DOI
10.3390/computers13040101