Classification of Facial Expressions Under Partial Occlusion for VR Games

• Type: Article in International Conference Proceedings Book
• Date: 2022

Authors

Rodrigues, ASF (Author) Other; Lopes, JC (Author) Other; Lopes, RP (Author) Other; Luis F Teixeira (Author) FEUP

Conference proceedings International

Title: OPTIMIZATION, LEARNING ALGORITHMS AND APPLICATIONS, OL2A 2022

Pages: 804-819

2nd International Conference on Optimization, Learning Algorithms and Applications (OL2A)

Povoa de Varzim, PORTUGAL, OCT 24-25, 2022

Indexing

ISI Web of Knowledge - 8 Citations

Scopus - 14 Citations

Other information

Authenticus ID: P-00X-PCN

DOI: 10.1007/978-3-031-23236-7_55

Abstract (EN): Facial expressions are one of the most common way to externalize our emotions. However, the same emotion can have different effects on the same person and has different effects on different people. Based on this, we developed a system capable of detecting the facial expressions of a person in real-time, occluding the eyes (simulating the use of virtual reality glasses). To estimate the position of the eyes, in order to occlude them, Multi-task Cascade Convolutional Neural Networks (MTCNN) were used. A residual network, a VGG, and the combination of both models, were used to perform the classification of 7 different types of facial expressions (Angry, Disgust, Fear, Happy, Sad, Surprise, Neutral), classifying the occluded and non-occluded dataset. The combination of both models, achieved an accuracy of 64.9% for the occlusion dataset and 62.8% for no occlusion, using the FER-2013 dataset. The primary goal of this work was to evaluate the influence of occlusion, and the results show that the majority of the classification is done with the mouth and chin. Nevertheless, the results were far from the state-of-the-art, which is expect to be improved, mainly by adjusting the MTCNN.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 16