scholarly journals Introductory Chapter: Face Recognition - Overview, Dimensionality Reduction, and Evaluation Methods

10.5772/63995 ◽  
2016 ◽  
Author(s):  
S. Ramakrishnan
Keyword(s):  
Face Recognition ◽  
Dimensionality Reduction ◽  
Evaluation Methods

scholarly journals Two Dimensionality Reduction Techniques for SURF Based Face Recognition

2016 ◽  
Vol 85 ◽  
pp. 241-248 ◽  
Author(s):  
A. Vinay ◽  
Vikkram Vasuki ◽  
Shreyas Bhat ◽  
K.S. Jayanth ◽  
K.N. Balasubramanya Murthy ◽  
...  
Keyword(s):  
Face Recognition ◽  
Dimensionality Reduction ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques

scholarly journals PCA-based dimensionality reduction for face recognition

2021 ◽  
Vol 19 (5) ◽  
pp. 1622
Author(s):  
Md. Abu Marjan ◽  
Md. Rashedul Islam ◽  
Md. Palash Uddin ◽  
Masud Ibn Afjal ◽  
Md. Al Mamun
Keyword(s):  
Face Recognition ◽  
Dimensionality Reduction

Evaluation Methods in Face Recognition

2005 ◽  
pp. 329-348 ◽  
Author(s):  
P. Jonathon Phillips ◽  
Patrick Grother ◽  
Ross Micheals
Keyword(s):  
Face Recognition ◽  
Evaluation Methods

A novel dimensionality-reduction approach for face recognition

2006 ◽  
Vol 69 (13-15) ◽  
pp. 1683-1687 ◽  
Author(s):  
Fengxi Song ◽  
David Zhang ◽  
Jingyu Yang
Keyword(s):  
Face Recognition ◽  
Dimensionality Reduction ◽  
Reduction Approach

scholarly journals AN ADOPTION OF 2D-PCA/ICA BASED POST-PROCESSING DIMENSIONALITY REDUCTION ALGORITHM FOR FACIAL RECOGNITION SYSTEM

2020 ◽  
Vol 3 (2) ◽  
pp. 222-235
Author(s):  
Vivian Nwaocha ◽  
◽  
Ayodele Oloyede ◽  
Deborah Ogunlana ◽  
Michael Adegoke ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Dimensionality Reduction ◽  
Nearest Neighbor ◽  
Gaussian Function ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Discrimination Ability ◽  
Face Images

Face images undergo considerable amount of variations in pose, facial expression and illumination condition. This large variation in facial appearances of the same individual makes most Existing Face Recognition Systems (E-FRS) lack strong discrimination ability and timely inefficient for face representation due to holistic feature extraction technique used. In this paper, a novel face recognition framework, which is an extension of the standard (PCA) and (ICA) denoted as two-dimensional Principal Component Analysis (2D-PCA) and two-dimensional Independent Component Analysis (2D-ICA) respectively is proposed. The choice of 2D was advantageous as image covariance matrix can be constructed directly using original image matrices. The face images used in this study were acquired from the publicly available ORL and AR Face database. The features belonging to similar class were grouped and correlation calculated in the same order. Each technique was decomposed into different components by employing multi-dimensional grouped empirical mode decomposition using Gaussian function. The nearest neighbor (NN) classifier is used for classification. The results of evaluation showed that the 2D-PCA method using ORL database produced RA of 92.5%, PCA produced RA of 75.00%, ICA produced RA of 77.5%, 2D-ICA produced RA of 96.00%. However, 2D-PCA methods using AR database produced RA of 73.56%, PCA produced RA of 62.41%, ICA produced RA of 66.20%, 2D-ICA method produced RA of 77.45%. This study revealed that the developed face recognition framework algorithm achieves an improvement of 18.5% and 11.25% for the ORL and AR databases respectively as against PCA and ICA feature extraction techniques. Keywords: computer vision, dimensionality reduction techniques, face recognition, pattern recognition

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