Facial expression recognition using lightweight deep learning modeling
Copyright policy )Facial expression recognition using lightweight deep learning modeling
<abstract><p>Facial expression is a type of communication and is useful in many areas of computer vision, including intelligent visual surveillance, human-robot interaction and human behavior analysis. A deep learning approach is presented to classify happy, sad, angry, fearful, contemptuous, surprised and disgusted expressions. Accurate detection and classification of human facial expression is a critical task in image processing due to the inconsistencies amid the complexity, including change in illumination, occlusion, noise and the over-fitting problem. A stacked sparse auto-encoder for facial expression recognition (SSAE-FER) is used for unsupervised pre-training and supervised fine-tuning. SSAE-FER automatically extracts features from input images, and the softmax classifier is used to classify the expressions. Our method achieved an accuracy of 92.50% on the JAFFE dataset and 99.30% on the CK+ dataset. SSAE-FER performs well compared to the other comparative methods in the same domain.</p></abstract>
- New York University United States
- Abbottabad University of Science and Technology Pakistan
- Zayed University United Arab Emirates
- Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology Pakistan
- Zhejiang Lab China (People's Republic of)
Facial expression, Artificial intelligence, stacked sparse auto-encoder, Social Sciences, Experimental and Cognitive Psychology, Speech recognition, Pattern recognition (psychology), Deep Learning, Facial Landmark Detection, Facial Expression Analysis, QA1-939, Image Processing, Computer-Assisted, facial expression recognition, Humans, Psychology, Face Recognition and Analysis Techniques, Facial recognition system, Feature Learning, Communication, deep learning, Deep learning, Fear, Computer science, FOS: Psychology, Facial Expression, machine learning, classification, Emotion Recognition, Computer Science, Physical Sciences, Computer Vision and Pattern Recognition, Face Recognition and Dimensionality Reduction Techniques, Facial expression recognition, Facial Recognition, TP248.13-248.65, Mathematics, Biotechnology, Emotion Recognition and Analysis in Multimodal Data
Facial expression, Artificial intelligence, stacked sparse auto-encoder, Social Sciences, Experimental and Cognitive Psychology, Speech recognition, Pattern recognition (psychology), Deep Learning, Facial Landmark Detection, Facial Expression Analysis, QA1-939, Image Processing, Computer-Assisted, facial expression recognition, Humans, Psychology, Face Recognition and Analysis Techniques, Facial recognition system, Feature Learning, Communication, deep learning, Deep learning, Fear, Computer science, FOS: Psychology, Facial Expression, machine learning, classification, Emotion Recognition, Computer Science, Physical Sciences, Computer Vision and Pattern Recognition, Face Recognition and Dimensionality Reduction Techniques, Facial expression recognition, Facial Recognition, TP248.13-248.65, Mathematics, Biotechnology, Emotion Recognition and Analysis in Multimodal Data
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