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Two-Dimensional Face Surface Analysis Using Facial Feature Points Detection Approaches

Two-Dimensional Face Surface Analysis Using Facial Feature Points Detection Approaches

Rachid Ahdid, Es-said Azougaghe, Said Safi, Bouzid Manaut
Copyright: © 2018 |Volume: 16 |Issue: 1 |Pages: 15
ISSN: 1539-2937|EISSN: 1539-2929|EISBN13: 9781522542360|DOI: 10.4018/JECO.2018010105
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MLA

Ahdid, Rachid, et al. "Two-Dimensional Face Surface Analysis Using Facial Feature Points Detection Approaches." JECO vol.16, no.1 2018: pp.57-71. http://doi.org/10.4018/JECO.2018010105

APA

Ahdid, R., Azougaghe, E., Safi, S., & Manaut, B. (2018). Two-Dimensional Face Surface Analysis Using Facial Feature Points Detection Approaches. Journal of Electronic Commerce in Organizations (JECO), 16(1), 57-71. http://doi.org/10.4018/JECO.2018010105

Chicago

Ahdid, Rachid, et al. "Two-Dimensional Face Surface Analysis Using Facial Feature Points Detection Approaches," Journal of Electronic Commerce in Organizations (JECO) 16, no.1: 57-71. http://doi.org/10.4018/JECO.2018010105

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Abstract

Geometrical features are widely used to descript human faces. Generally, they are extracted punctually from landmarks, namely facial feature points. The aims are various, such as face recognition, facial expression recognition, face detection. In this article, the authors present two feature extraction methods for two-dimensional face recognition. Their approaches are based on facial feature points detection by compute the Euclidean Distance between all pairs of this points for a first method (ED-FFP) and Geodesic Distance in the second approach (GD-FFP). These measures are employed as inputs to commonly used classification techniques such as Neural Networks (NN), k-Nearest Neighbor (KNN) and Support Vector Machines (SVM). To test the methods and evaluate its performance, a series of experiments were performed on two-dimensional face image databases (ORL and Yale). The experimental results also indicated that the extraction of image features is computationally more efficient using Geodesic Distance than Euclidean Distance.

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