On the Performance Analysis of APIs Recognizing Emotions from Video Images of Facial Expressions

被引：5
|
作者：
Bhattacharjee, Ananya [1 ]
Pias, Tanmoy [1 ]
Ahmad, Mahathir [1 ]
Rahman, Ashikur [1 ]
机构：
[1] Bangladesh Univ Engn & Technol, Dept CSE, Dhaka 1000, Bangladesh
来源：
2018 17TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA) | 2018年
关键词：
Facial Expression Recognition; Emotion; API; Performance; RECOGNITION;
D O I：
10.1109/ICMLA.2018.00040
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This work is meant to provide insights towards state-of-the-art Application Programming Interfaces (APIs) commercially available today for recognizing emotions from video footprint of facial expressions. We analyze and compare performance of four such integrable commercial APIs using standard Extended Cohn-Kanade Dataset (CK+) containing over 10, 000 images with facial emotions and some randomly collected real life images. We also discuss issues to understand their limitations and adaptation as well as enhancement strategies. For performance analysis, we introduce the performance metric moving average to find the primary expression in the displayed emotion of a video sequence. Finally, using the popular valence-arousal dimensions, we show how to (lexicographically) order six basic emotions anger, happiness, sadness, surprise, fear and disgust. By providing neutral performance comparison, we hope to fill the existing notable gap between researchers, solution providers and application developers working on emotion based user modeling.
引用
收藏
页码：223 / 230
页数：8
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