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

Rachid Ahdid, Es-said Azougaghe, Said Safi and Bouzid Manaut
Additional contact information
Rachid Ahdid: Department of Mathematics and Informatics, Sultan Moulay Slimane University, Beni Mellal, Morocco
Es-said Azougaghe: Department of Mathematics and Informatics, Sultan Moulay Slimane University, Beni Mellal, Morocco
Said Safi: Department of Mathematics and Informatics, Sultan Moulay Slimane University, Beni Mellal, Morocco
Bouzid Manaut: Departement of Physics, Sultan Moulay Slimane University, Beni Mellal, Morocco

Journal of Electronic Commerce in Organizations (JECO), 2018, vol. 16, issue 1, 57-71

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.

Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 4018/JECO.2018010105 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jeco00:v:16:y:2018:i:1:p:57-71

Access Statistics for this article

Journal of Electronic Commerce in Organizations (JECO) is currently edited by Pedro Isaías

More articles in Journal of Electronic Commerce in Organizations (JECO) from IGI Global
Bibliographic data for series maintained by Journal Editor ().