The current challenges of automatic recognition of facial expressions: A systematic review

Abstract

In recent years, due to its great economic and social potential, the recognition of facial expressions linked to emotions has become one of the most flourishing applications in the field of artificial intelligence, and has been the subject of many developments. However, despite significant progress, this field is still subject to many theoretical debates and technical challenges. It therefore seems important to make a general inventory of the different lines of research and to present a synthesis of recent results in this field. To this end, we have carried out a systematic review of the literature according to the guidelines of the PRISMA method. A search of 13 documentary databases identified a total of 220 references over the period 2014–2019. After a global presentation of the current systems and their performance, we grouped and analyzed the selected articles in the light of the main problems encountered in the field of automated facial expression recognition. The conclusion of this review highlights the strengths, limitations and main directions for future research in this field.

Academic Emotion Classification Using FER: A Systematic Review

“…Furthermore, the algorithms are developed for data exploitation and extrapolate stereotypical traits, which precludes them from considering exceptional cases and uncommon configurations [116]. According to cutting-edge theoretical perspectives, emotions are a nuanced and dynamic phenomenon that vary along many parameters that have not yet been completely formalized in theory.…”

A Review of Emotion Recognition Methods From Keystroke, Mouse, and Touchscreen Dynamics

“…They can be generally classified into three categories: a) Use external body signals for emotion recognition, including facial expression, body gestures, gait, eyetracking, etc. These signals can be easily noticed by others but not always reflect one's real emotional states [15][16][17][18][19][20][21][22][23]. b) Use internal physiological signals such as heart rate, sphygmic, skin conductance, blood pressure, Electroencephalography (EEG), etc.…”

“…c) Utilize other contextual signals other than body signal themselves such as voice, text content, KMT dynamics, which can be collected non-intrusively and unobtrusively [32][33][34][35][36][37][38][39]. There have been some systematic surveys or reviews in the field of emotion recognition from facial expression [17], body gestures [18,19], eye-tracking [22], internal physiological signals [25,27], voice [32][33][34], text [35,36], so readers interested in the related topics can refer to them. Thus, this paper will not detail them.…”