Facial emotion recognition using average face ratios and fuzzy hamming distance

Facial emotion recognition (FER) is an important topic in the fields of computer vision and artificial intelligence owing to its significant academic and commercial potential. Nowadays, emotional factors are important as classic functional aspects of customer purchasing behavior. Purchasing choices and decisions making are the result of a careful analysis of the product advantages and disadvantages and of affective and emotional aspects. This paper presents a novel method for human emotion classification and recognition. We generate seven referential faces suitable for each kind of facial emotion based on perfect face ratios and some classical averages. The basic idea is to extract perfect face ratios for emotional face and for each referential face as features and calculate the distance between them by using fuzzy hamming distance. To extract perfect face ratios, we use the point landmarks in the face then sixteen features will be extract. An experimental evaluation demonstrates the satisfactory performance of our approach on WSEFEP dataset. It can be applied with any existing facial emotion dataset. The proposed algorithm will be a competitor of the other proposed relative approaches. The recognition rate reaches more than 90%.