scholarly journals Multimodal Face Recognition System Using Spectral Transformation of 2D Texture Feature and Statistical Processing of Face Range Images

2015 ◽  
Vol 46 ◽  
pp. 1537-1545 ◽  
Author(s):  
S. Naveen ◽  
R.S. Moni
Keyword(s):  
Face Recognition ◽  
Texture Feature ◽  
Statistical Processing ◽  
Recognition System ◽  
Range Images ◽  
Spectral Transformation ◽  
Face Recognition System ◽  
Multimodal Face Recognition

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

Research and Design of Fast Special Human Face Recognition System

2020 ◽  
Author(s):  
Rachmat Muwardi ◽  
Huangyao Qin ◽  
Hongmin Gao ◽  
Harun Usman Ghifarsyam ◽  
Muhammad Hafizd Ibnu Hajar ◽  
...  
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Human Face ◽  
Human Face Recognition ◽  
Face Recognition System ◽  
Research And Design

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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