Fusion of Features and Extreme Learning Machine for Facial Expression Recognition

Abstract

Human emotion is highly correlated to facial expressions. Due to its growing demand in different sectors, an emotion recognition method is proposed through recognizing facial expressions. The input image is preprocessed and then the resulting image is segmented into four facial expression regions following the newly proposed segmentation method. Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) are fused to extract the necessary features from the four segmented parts. The dimension of the feature vector is reduced using Principal Component Analysis (PCA). To classify the expressions, Extreme Learning Machine (ELM) is used. For evaluating the performance of the proposed method, three widely used and publicly available facial expression datasets (JAFFE, CK+, RaFD) are used. The proposed method achieved 95.3%, 99.84% and 98.65% accuracy while using images from JAFFE, CK+ and RaFD dataset respectively. Performance of the proposed method on these datasets is compared to other facial expression recognition methods on these datasets to indicate that the proposed method achieves state-of-the-art performance.

Facial expression recognition method based on PSA—YOLO network

“…Literature Jan et al ( 2018 ) defines six basic terms of “anger,” “disgust,” “fear,” “happiness,” “sadness,” and “surprise,” followed by the expression of “contempt.” Facial expression recognition is a traditional problem in computer vision and an essential part of artificial intelligence technology. It has gradually attracted more and more attention, and scholars have proposed a large number of new methods (Islam and Hossain, 2019 ).…”

“…Facial expression recognition is a traditional problem in computer vision and an essential part of artificial intelligence technology. It has gradually attracted more and more attention, and scholars have proposed a large number of new methods (Islam and Hossain, 2019).…”