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Title: SVM Point-Based Real-time Emotion Detection
Authors: SWINKELS, Wout 
Xiao, Feng
Shen, Haibin
Issue Date: 2017
Publisher: IEEE
Source: Proceedings of the 2017 Conference on Dependable and Secure Computing,p. 86-92 (Art N° B1-3)
Abstract: Face recognition is nowadays implemented in security systems to grant access to areas that are only allowed for authorized persons. However an additional layer of security can be added to these systems by detemining if the person in front of the camera is present in real-life and that the detected object is not a 2D representation of that person. Forcing people to interact with the system by for example posing a certain emotion can be an additional layer of complexity to deny the access for unauthorized persons. This paper focuses on that aspect i.e. real-time emotion detection. Therefore a novel algorithm is developed to extract emotions based on the movement of 19 feature points. These feature points are located in different regions of the countenance such as the mouth, eyes, eyebrows and nose. To obtain the feature points an Ensemble of Regression Trees [1] is constructed. After the extraction of the feature points 12 distances, in and around these facial regions, are calculated to be used in displacement ratios. In the final step, the algorithm inputs the displacement ratios to a classification algorithm, which is a cascade of a multi-class support vector machine (SVM) and a binary SVM. Experimental results on the Extended Cohn-Kanade dataset (CK+) [2], [3] indicate that the proposed algorithm reaches an average accuracy of 89,78% at a detection speed of less than 30 ms. The accuracy is comparable with stateof- the-art emotion detection algorithms and outperforms these algorithms when detecting the emotions Contempt, Disgust, Fear and Surprise. The detection speed evaluation of the proposed algorithm was perfomed on a Windows 8.1 laptop with an Intel-Core i7-5500U CPU (2.40 GHz) and 8,00GB of RAM.
Notes: Swinkels, W (reprint author), Univ Hasselt, Fac Engn Technol, Diepenbeek, Belgium.
Keywords: HoG; ensemble of Regression Trees; SVM; emotion detection; real-time
Document URI:
ISBN: 9781509055685
DOI: 10.1109/DESEC.2017.8073838
ISI #: 000450296400009
Rights: (c) 2017 IEEE
Category: C1
Type: Proceedings Paper
Validations: ecoom 2019
Appears in Collections:Research publications

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