Evaluation of Two Feature Extraction Techniques for Age-Invariant Face Recognition

2020 ◽  
pp. 193-205
Author(s):  
Ashutosh Dhamija ◽  
R. B. Dubey
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Extraction Techniques

scholarly journals Hybrid Approach for Face Recognition from a Single Sample per Person by Combining VLC and GOM

2019 ◽  
Vol 29 (1) ◽  
pp. 1523-1534 ◽  
Author(s):  
Ahmed Ghorbel ◽  
Walid Aydi ◽  
Imen Tajouri ◽  
Nouri Masmoudi
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Execution Time ◽  
Hybrid Approach ◽  
Recognition System ◽  
Single Sample ◽  
Data Sets ◽  
Extraction Techniques ◽  
Face Recognition System ◽  
Execution Speed

Abstract This paper proposes a new face recognition system based on combining two feature extraction techniques: the Vander Lugt correlator (VLC) and Gabor ordinal measures (GOM). The proposed system relies on the execution speed of VLC and the robustness of GOM. In this system, we applied the Tan and Triggs and retina modeling enhancement techniques, which are well suited for VLC and GOM, respectively. We evaluated our system on the standard FERET probe data sets and on extended YaleB database. The obtained results exhibited better face recognition rates in a shorter execution time compared to the GOM technique.

scholarly journals Membership-Degree Preserving Discriminant Analysis with Applications to Face Recognition

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Zhangjing Yang ◽  
Chuancai Liu ◽  
Pu Huang ◽  
Jianjun Qian
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Discriminant Analysis ◽  
Nearest Neighbor ◽  
High Dimensional ◽  
Membership Degree ◽  
Extraction Techniques ◽  
Fisher Criterion ◽  
Extraction Algorithm ◽  
Definition Of

In pattern recognition, feature extraction techniques have been widely employed to reduce the dimensionality of high-dimensional data. In this paper, we propose a novel feature extraction algorithm called membership-degree preserving discriminant analysis (MPDA) based on the fisher criterion and fuzzy set theory for face recognition. In the proposed algorithm, the membership degree of each sample to particular classes is firstly calculated by the fuzzyk-nearest neighbor (FKNN) algorithm to characterize the similarity between each sample and class centers, and then the membership degree is incorporated into the definition of the between-class scatter and the within-class scatter. The feature extraction criterion via maximizing the ratio of the between-class scatter to the within-class scatter is applied. Experimental results on the ORL, Yale, and FERET face databases demonstrate the effectiveness of the proposed algorithm.

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