End to End Deep Learning for Single Step Real-Time Facial Expression Recognition

Reddy, B.Jathin; Kim, Ye-Hoon; Yun, Sojung; Jang, Junik; Hong, Sheng

doi:10.1007/978-3-319-56687-0_8

Cited by 2 publications

(1 citation statement)

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“…In traditional methods, feature extraction and feature classification steps are independent. Compared to recent research, [13], these machine learning techniques for facial expression recognition are considered as classical techniques.…”

Section: Astesj Issn: 2415-6698mentioning

confidence: 99%

A New Video Based Emotions Analysis System (VEMOS): An Efficient Solution Compared to iMotions Affectiva Analysis Software

Jmour¹,

Masmoudi²,

Abdelkrim³

2021

Adv. sci. technol. eng. syst. j.

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The Micro-facial expression is the most effective way to display human emotional state. But it needs an expert coder to be decoded. Recently, new computer vision technologies have emerged to automatically extract facial expressions from human faces. In this study, a videobased emotion analysis system is implemented to detect human faces and recognize their emotions from recorded videos. Relevant information is presented on graphs and can be viewed on video to help understanding expressed emotions responses. The system recognizes and analyzes emotions frame by frame. The image-based facial expressions model used deep learning methods. It was tested with two pre-trained models on two different databases. To validate the video-based emotion analysis system, the aim of this study is to challenge it by comparing the performance of the initial implemented model to the iMotions Affectiva AFFDEX emotions analysis software on labeled sequences. These sequences were recorded and performed by a Tunisian actor and validated by an expert psychologist. Emotions to be recognized correspond to the six primary emotions defined by Paul Ekman : anger, disgust, fear, joy, sadness, surprise, and then their possible combinations according to Robert Plutchik's psycho-evolutionary theory of emotions. Results show a progressive increase of the system's performance, achieving a high correlation with Affectiva. Joy, surprise and disgust expressions can reliably be detected with an underprediction of anger from the two systems. The implemented system has shown more efficient results on recognizing sadness, fear and secondary emotions. Contrary to iMotions Affectiva analysis results, VEMOS system has recognized correctly sadness and contempt. It has also successfully recognized surprsie and fear and detect the alarm secondary emotion. iMotions Affectiva has confused surprise and fear. Finally, compared to iMotions the system was also able to detect peak of morbidness and remorse secondary emotions.

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Section: Astesj Issn: 2415-6698mentioning

confidence: 99%