Facial emotion recognition using min-max similarity classifier

TL;DR

This paper introduces a simple yet effective facial emotion recognition method using pixel normalization and a Min-Max classifier, achieving high accuracy and outperforming existing template matching techniques.

Contribution

It proposes a novel, computationally simple emotion recognition algorithm that reduces inter-class pixel mismatch using pixel normalization and Min-Max metric in a nearest neighbor classifier.

Findings

Recognition accuracy improved from 92.85% to 98.57%.

Outperforms existing template matching methods.

Effective in handling variability in facial features and recording conditions.

Abstract

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.