Efficient face recognition system based on luminance distribution by using maximum likelihood

2010 ◽  
Author(s):  
Ching-Tang Hsieh ◽  
Chia-Shing Hu
Keyword(s):  
Face Recognition ◽  
Maximum Likelihood ◽  
Recognition System ◽  
Luminance Distribution ◽  
Face Recognition System

A Robust and Efficient Face Recognition System Based on Luminance Distribution Using Maximum Likelihood

2012 ◽  
Vol 241-244 ◽  
pp. 1705-1709
Author(s):  
Ching Tang Hsieh ◽  
Chia Shing Hu
Keyword(s):  
Face Recognition ◽  
Maximum Likelihood ◽  
Recognition Rate ◽  
Computation Time ◽  
Likelihood Estimation ◽  
Recognition System ◽  
Luminance Distribution ◽  
Face Region ◽  
Face Recognition System ◽  
The Face

In this paper, a robust and efficient face recognition system based on luminance distribution by using maximum likelihood estimation is proposed. The distribution of luminance components of the face region is acquired and applied to maximum likelihood test for face matching. The experimental results showed that the proposed method has a high recognition rate and requires less computation time.

FACE DETECTION AND RECOGNITION USING MAXIMUM LIKELIHOOD CLASSIFIERS ON GABOR GRAPHS

2009 ◽  
Vol 23 (03) ◽  
pp. 433-461 ◽  
Author(s):  
MANUEL GÜNTHER ◽  
ROLF P. WÜRTZ
Keyword(s):  
Face Recognition ◽  
Maximum Likelihood ◽  
Face Detection ◽  
Recognition System ◽  
Similarity Functions ◽  
Expression Variation ◽  
Face Recognition System ◽  
Face Detection And Recognition ◽  
Ml Estimator ◽  
Detection And Recognition

We present an integrated face recognition system that combines a Maximum Likelihood (ML) estimator with Gabor graphs for face detection under varying scale and in-plane rotation and matching as well as a Bayesian intrapersonal/extrapersonal classifier (BIC) on graph similarities for face recognition. We have tested a variety of similarity functions and achieved verification rates (at FAR 0.1%) of 90.5% on expression-variation and 95.8% on size-varying frontal images within the CAS-PEAL database. Performing Experiment 1 of FRGC ver2.0, the method achieved a verification rate of 72%.

scholarly journals Design of face recognition system based on CNN

2020 ◽  
Vol 1601 ◽  
pp. 052011
Author(s):  
Yong Li ◽  
Zhe Wang ◽  
Yang Li ◽  
Xu Zhao ◽  
Hanwen Huang
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Face Recognition System

HUMAN FACE RECOGNITION FROM A SINGLE FRONT VIEW

1992 ◽  
Vol 06 (04) ◽  
pp. 571-593 ◽  
Author(s):  
CHING-WEN CHEN ◽  
CHUNG-LIN HUANG
Keyword(s):  
Face Recognition ◽  
Active Contour Model ◽  
Facial Feature ◽  
Recognition System ◽  
Facial Features ◽  
Human Face Recognition ◽  
Front View ◽  
Deformable Template ◽  
Face Recognition System ◽  
Feature Values

This paper presents a face recognition system which can identify the unknown identity effectively using the front-view facial features. In front-view facial feature extractions, we can capture the contours of eyes and mouth by the deformable template model because of their analytically describable shapes. However, the shapes of eyebrows, nostrils and face are difficult to model using a deformable template. We extract them by using the active contour model (snake). After the contours of all facial features have been captured, we calculate effective feature values from these extracted contours and construct databases for unknown identities classification. In the database generation phase, 12 models are photographed, and feature vectors are calculated for each portrait. In the identification phase if any one of these 12 persons has his picture taken again, the system can recognize his identity.

scholarly journals Face Recognition System for Set-Top Box-Based Intelligent TV

Sensors ◽  
2014 ◽  
Vol 14 (11) ◽  
pp. 21726-21749 ◽  
Author(s):  
Won Lee ◽  
Yeong Kim ◽  
Hyung Hong ◽  
Kang Park
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Set Top Box ◽  
Face Recognition System
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