scholarly journals Automatic Face Recognition System using Pattern Recognition Techniques: A Survey

2013 ◽  
Vol 83 (5) ◽  
pp. 10-13 ◽  
Author(s):  
Ningthoujam SunitaDevi ◽  
K Hemachandran
Keyword(s):  
Pattern Recognition ◽  
Face Recognition ◽  
Recognition System ◽  
Pattern Recognition Techniques ◽  
Face Recognition System

scholarly journals Orchestration of containers of microservice architecture in face recognition system

2020 ◽  
pp. 108-114
Author(s):  
Andrey Filipiev ◽  
Tatyana Makhalkina ◽  
Dmitry Dmitriev
Keyword(s):  
Pattern Recognition ◽  
Face Recognition ◽  
Recognition System ◽  
Load Management ◽  
Face Recognition System ◽  
Microservice Architecture ◽  
Physical Boundary

This paper solved the problem of applying the technology of containers orchestration in solving one of the problems of pattern recognition, namely face recognition. Due to the fact that face recognition technology has grown to such a level that it can be used for commercial purposes, many companies are beginning to actively implement such solutions. The use of microservice architecture allowed independent deployment and scaling of each service, as well as a clear physical boundary between the services. Containerization and orchestration technologies were used to create a more secure application, facilitate deployment, improve scalability and load management.

scholarly journals Classes of Unique Face and Its Classification

2013 ◽  
Vol 7 (1) ◽  
pp. 968-973
Author(s):  
Raghavendra Kulkarni ◽  
Dr. P. Nageswar Rao
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Face Recognition ◽  
Law Enforcement ◽  
Recognition System ◽  
Singular Value ◽  
Control Information ◽  
Face Recognition System ◽  
Number Of Faces

Near resembling faces ,Look alike faces, disguised faces and many more are todays challenges for researchers in the field of face recognition and these challenges become more serious in case of large facial Variations. Humans are able to identify reliably a large number of faces but a automated face recognition system must be face specific, it should effectively use features that discriminate a face from others by preferably amplifying distinctive characteristics of face. Face recognition has drawn wide attention from researchers in areas of machine learning, computer vision, pattern recognition, neural networks, access control, information security, law enforcement and surveillance, smart cards etc. This paper shows that how the unique face which is having a unique singular value per face under different variations is effectively classified and recognized.

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.

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