A Review: Facial Expression Detection with its Techniques and Application

Abstract

Facial expression recognition performs a critical role in the human-machine interaction area.

On Facial Expression Recognition Benchmarks

“…Feature extraction is mostly considered the second and most important step in facial expression recognition as the selection of the features is an important task. It helps in representing the facial image effectively by extracting the subtle changes of a facial image into a feature vector [40,49]. e results displayed in Figure 6 shows that local binary pattern (LBP) is the most commonly used feature extraction method and it accounts for 22.9% of all the twenty-nine ( 29) methods used by researchers in the period.…”

“…e distribution of the consistently used algorithms is shown in Figure 8. Ideally, classification is the final stage in facial expression recognition [40]. e classifier must be trained to categorize expressions into sadness, anger, fear, happiness, disgust, surprise, neutral, and sometimes other emotions like joy and smiling [27,41].…”

“…Although there has been considerable number of surveys and literature on facial expression classification, to the best of our knowledge, there is not any comprehensive systematic review in the field of facial expression classification. Having evaluated the above surveys and reviews using an evaluation checklist by Kitchenham [31], it was observed that majority of the authors conducted a narrative review instead of a systematic review, providing general information on the various aspects of facial expression classification analysis 2 Applied Computational Intelligence and Soft Computing [32][33][34][35][36][37][38][39][40][41][42]. Moreover, to the best of our knowledge, there is no existing systematic review on facial expression classification, which motivates our work as existing literature sought to examine the general trend of reviewing the most utilized methods.…”