Facial expression recognition by re-ranking with global and local generic features
Duc Minh Vo, Akihiro Sugimoto, Thái Hoàng Lê · 2016
Recognizing the facial expression plays an important role in human computer interaction. Following the recent success of the Convolutional Neural Network (CNN) in image classification and object recognition, this paper proposes a facial expression recognition method that makes full use of CNNs to detect face features globally and locally and that combines global and local generic features for improving accuracy in recognition. Our method uses global generic features with the Support Vector Machine (SVM) classifier to generate most plausible candidates in expression class while local generic features with the SVM classifier to look into the candidates to re-rank them for recognition. Experimental results using data-sets available in public support the effectiveness of our proposed method by demonstrating improved accuracy against the state-of-the-arts.