Facial emotion recognition using min-max similarity classifier
This work addresses the need for effective and computationally simple feature selection and classification in facial emotion recognition, which is useful for human-computer interaction applications, but it is incremental as it builds on existing template matching methods.
The paper tackled the problem of automatic facial emotion recognition by proposing a Min-Max similarity classifier to reduce inter-class pixel mismatch, resulting in an improvement from 92.85% to 98.57% accuracy on the JAFFE database.
Recognition of human emotions from the imaging templates is useful in a wide variety of human-computer interaction and intelligent systems applications. However, the automatic recognition of facial expressions using image template matching techniques suffer from the natural variability with facial features and recording conditions. In spite of the progress achieved in facial emotion recognition in recent years, the effective and computationally simple feature selection and classification technique for emotion recognition is still an open problem. In this paper, we propose an efficient and straightforward facial emotion recognition algorithm to reduce the problem of inter-class pixel mismatch during classification. The proposed method includes the application of pixel normalization to remove intensity offsets followed-up with a Min-Max metric in a nearest neighbor classifier that is capable of suppressing feature outliers. The results indicate an improvement of recognition performance from 92.85% to 98.57% for the proposed Min-Max classification method when tested on JAFFE database. The proposed emotion recognition technique outperforms the existing template matching methods.