Facial Expression Recognition using Visual Saliency and Deep Learning
We have developed a convolutional neural network for the purpose of recognizing facial expressions in human beings. We have fine-tuned the existing convolutional neural network model trained on the visual recognition dataset used in the ILSVRC2012 to two widely used facial expression datasets - CFEE and RaFD, which when trained and tested independently yielded test accuracies of 74.79% and 95.71%, respectively. Generalization of results was evident by training on one dataset and testing on the other. Further, the image product of the cropped faces and their visual saliency maps were computed using Deep Multi-Layer Network for saliency prediction and were fed to the facial expression recognition CNN. In the most generalized experiment, we observed the top-1 accuracy in the test set to be 65.39%. General confusion trends between different facial expressions as exhibited by humans were also observed.
Code (0)
등록된 구현이 없습니다.
Tasks
Deep LearningFacial Expression RecognitionFacial Expression Recognition (FER)Saliency PredictionSimilar Papers 제목 키워드 기반
Visual Saliency Maps Can Apply to Facial Expression Recognition
Human eyes concentrate different facial regions during distinct cognitive activities. We study utilising facial visual saliency maps to classify different facial expressions into different emotions. Our results show that…
Facial Expression RecognitionFacial Expression Recognition (FER)General ClassificationSaliency PredictionSAF- BAGE: Salient Approach for Facial Soft-Biometric Classification - Age, Gender, and Facial Expression
How can we improve the facial soft-biometric classification with help of the human visual system? This paper explores the use of saliency which is equivalent to the human visual system to classify Age, Gender and Facial …
Age And Gender ClassificationClassificationGender ClassificationGeneral ClassificationFacial Expression Recognition with Swin Transformer
The task of recognizing human facial expressions plays a vital role in various human-related systems, including health care and medical fields. With the recent success of deep learning and the accessibility of a large am…
Facial Expression RecognitionFacial Expression Recognition (FER)Saliency-guided Emotion Modeling: Predicting Viewer Reactions from Video Stimuli
Understanding the emotional impact of videos is crucial for applications in content creation, advertising, and Human-Computer Interaction (HCI). Traditional affective computing methods rely on self-reported emotions, fac…
Knowledge-Enhanced Facial Expression Recognition with Emotional-to-Neutral Transformation
Existing facial expression recognition (FER) methods typically fine-tune a pre-trained visual encoder using discrete labels. However, this form of supervision limits to specify the emotional concept of different facial e…
Facial Expression RecognitionFacial Expression Recognition (FER)