Abstract
Herein, a three-stage support vector machine (SVM) for facial expression recognition is proposed. The first stage comprises 21 SVMs, which are all the binary combinations of seven expressions. If one expression is dominant, then the first stage will suffice; if two are dominant, then the second stage is used; and, if three are dominant, the third stage is used. These multilevel stages help reduce the possibility of experiencing an error as much as possible. Different image preprocessing stages are used to ensure that the features attained from the face detected have a meaningful and proper contribution to the classification stage. Facial expressions are created as a result of muscle movements on the face. These subtle movements are detected by the histogram-oriented gradient feature, because it is sensitive to the shapes of objects. The features attained are then used to train the three-stage SVM. Two different validation methods were used: the leave-one-out and K-fold tests. Experimental results on three databases (Japanese Female Facial Expression, Extended Cohn-Kanade Dataset, and Radboud Faces Database) show that the proposed system is competitive and has better performance compared with other works.
Highlights
Artificial intelligence has created higher standards for innovation and has introduced new possibilities for human–computer interaction
Leave-one-out validation test The leave-one-out validation test is used to determine the accuracy of the system by dividing the dataset into
In the K-fold validation test, the dataset that has N images is divided into K sets
Summary
Artificial intelligence has created higher standards for innovation and has introduced new possibilities for human–computer interaction. There is a need to develop an accurate facial expression recognition (FER) system that can establish efficient communication between humans and computers. The development of such a system can be useful in several areas, such as lie detectors, surveillance, smart computing, visual development, computer gaming, and augmented reality [2]. For efficient communication to occur between two people, seven expressions are globally identified and analyzed to make communication smooth and reliable. Translating this idea into a computer system, the FER system can improve the way computers interact with humans and lead to further advancements in this field
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