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

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

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) and Bouzid Manaut (Departement of Physics, Sultan Moulay Slimane University, Beni Mellal, Morocco)
Copyright: © 2018 |Pages: 15
DOI: 10.4018/JECO.2018010105
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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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1. Introduction

Biometric face recognition technology has received significant attention in the past several years due to its potential in different applications. Automated human face recognition was applied in different fields including automated secured access to machines and buildings, automatic surveillance, forensic analysis, fast retrieval of records from databases in police departments, automatic identification of patients in hospitals, checking for fraud or identity and human-computer interaction (Gawali, 2014; Bedoui, 2008).

In recent years face recognition has received substantial attention from both research communities and the market, but still remained very challenging in real applications. Several face recognition algorithms have been developed during the past decades. Automatic recognition of human faces based on the 2D images processing is well developed this last years, and several techniques have been proposed. We find several global, local and hybrids methods: The Principal Component Analysis PCA also known under the name eigenfaces (Sirovich, 1990; Alain, 2009), two-dimensional version of PCA noted 2DPCA (Yang, 2004). The Stochastic Approach in (Laskri, 2002, Suri, 2011). In 1991, M. A. Turk et al. implemented the Principal Component Analysis (PCA) approach known under the name Eigenfaces (Turk, 1991). In 2001, G.D. Guo proposed a multi-class classification problem for a K-class classification test, Optimal-Pairwise Coupling (O-PWC) SVM (Guo, 2001). In 2003, J. Lu et al. implemented a method combines the strengths of the D-LDA and F-LDA approaches, while at the same time overcomes their shortcomings and limitations (Lu, 2003). Two-dimensional version (2DPCA) was presented by J. Yang et al. in 2004 for image representation (Yang, 2004). M. Visani et al. proposed Two-Dimensional Linear Discriminant Analysis (2DO-LDA) in 2004. This approach is chosen to jointly maximize the mean variation between classes and minimize the mean of the variations inside each class (Visani, 2004). H. Cevikalp et al. propose an approach called the Discriminative Common Vector method based on a variation of Fishers Linear Discriminant Analysis for the small sample size case in 2005 (Cevikalp, 2005). Linear Discriminant Analysis LDA also known under the name Fisherfaces was proposed by L. Bedoui et al. in 2008 (Bedoui, 2008). In 2010 M. Agarwalet al. implemented a method based on Principal Component Analysis (PCA) and Neural Network (NN) (Agarwal, 2010). In 2012 V.More et al, used modified fisher face and fuzzy fisher face FFLD for person identification (More, 2012). In 2014 W. Xu et al. propose an integrated algorithm based on the respective advantages of wavelets transform (WT), 2D Principle Component Analysis (PCA) and Support Vector Machines (SVM) (Xu et al., 2014).

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