scholarly journals Face Recognition using Block-Based DCT Feature Extraction

2012 ◽  
Vol 1 (4) ◽  
Author(s):  
MANIKANTAN K ◽  
Vaishnavi Govindarajan ◽  
Sasi Kiran V V S ◽  
Ramachandran S
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Block Based

scholarly journals Face Recognition Using Block Based Feature Extraction with CZT and Goertzel-algorithm as a Preprocessing Technique

2015 ◽  
Vol 46 ◽  
pp. 1458-1467 ◽  
Author(s):  
K.K. Varadarajan ◽  
P.R. Suhasini ◽  
K. Manikantan ◽  
S. Ramachandran
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Goertzel Algorithm ◽  
Block Based ◽  
Preprocessing Technique

Feature Extraction Using Block-based Local Binary Pattern for Face Recognition

2016 ◽  
Vol 2016 (10) ◽  
pp. 1-6 ◽  
Author(s):  
A. Moujahid ◽  
A. Abanda ◽  
F. Dornaika
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Local Binary Pattern ◽  
Block Based

Block based curvelet feature extraction for face recognition

2009 ◽  
Author(s):  
Mohamed El Aroussi ◽  
Mohammed El Hassouni ◽  
Sanaa Ghouzali ◽  
Mohammed Rziza ◽  
Driss Aboutajdine
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Block Based

Novel local feature extraction for age invariant face recognition

2021 ◽  
Vol 175 ◽  
pp. 114786
Author(s):  
Rajesh Kumar Tripathi ◽  
Anand Singh Jalal
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Local Feature ◽  
Local Feature Extraction

Kernel Null Space Marginal Fisher Analysis for Face Recognition

2014 ◽  
Vol 889-890 ◽  
pp. 1065-1068
Author(s):  
Yu’e Lin ◽  
Xing Zhu Liang ◽  
Hua Ping Zhou
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Sample Size ◽  
High Performance ◽  
Null Space ◽  
Small Sample Size ◽  
Recognition Rate ◽  
Original Data ◽  
Small Sample ◽  
Neighborhood Structure

In the recent years, the feature extraction algorithms based on manifold learning, which attempt to project the original data into a lower dimensional feature space by preserving the local neighborhood structure, have drawn much attention. Among them, the Marginal Fisher Analysis (MFA) achieved high performance for face recognition. However, MFA suffers from the small sample size problems and is still a linear technique. This paper develops a new nonlinear feature extraction algorithm, called Kernel Null Space Marginal Fisher Analysis (KNSMFA). KNSMFA based on a new optimization criterion is presented, which means that all the discriminant vectors can be calculated in the null space of the within-class scatter. KNSMFA not only exploits the nonlinear features but also overcomes the small sample size problems. Experimental results on ORL database indicate that the proposed method achieves higher recognition rate than the MFA method and some existing kernel feature extraction algorithms.

scholarly journals PEM-PCA: A Parallel Expectation-Maximization PCA Face Recognition Architecture

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Kanokmon Rujirakul ◽  
Chakchai So-In ◽  
Banchar Arnonkijpanich
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Expectation Maximization ◽  
High Speed ◽  
Expectation Maximization Algorithm ◽  
Parallel Architecture ◽  
Principal Component ◽  
Eigenvalue Decomposition ◽  
Computational Time ◽  
Recognition Systems

Principal component analysis or PCA has been traditionally used as one of the feature extraction techniques in face recognition systems yielding high accuracy when requiring a small number of features. However, the covariance matrix and eigenvalue decomposition stages cause high computational complexity, especially for a large database. Thus, this research presents an alternative approach utilizing an Expectation-Maximization algorithm to reduce the determinant matrix manipulation resulting in the reduction of the stages’ complexity. To improve the computational time, a novel parallel architecture was employed to utilize the benefits of parallelization of matrix computation during feature extraction and classification stages including parallel preprocessing, and their combinations, so-called a Parallel Expectation-Maximization PCA architecture. Comparing to a traditional PCA and its derivatives, the results indicate lower complexity with an insignificant difference in recognition precision leading to high speed face recognition systems, that is, the speed-up over nine and three times over PCA and Parallel PCA.

An Enhanced Fractal Dimension Based Feature Extraction for Thermal Face Recognition

2018 ◽  
pp. 217-226 ◽  
Author(s):  
Sandip Joardar ◽  
Arnab Sanyal ◽  
Dwaipayan Sen ◽  
Diparnab Sen ◽  
Amitava Chatterjee
Keyword(s):  
Feature Extraction ◽  
Fractal Dimension ◽  
Face Recognition ◽  
Thermal Face

Block-Based Face Recognition Using WLD

2013 ◽  
pp. 811-822
Author(s):  
Ting Rui ◽  
Zijian Yang ◽  
Fan Liu ◽  
Suqiang Jiang ◽  
Huabing Li
Keyword(s):  
Face Recognition ◽  
Block Based
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