Research on a face recognition system by the genetic algorithm

2007 ◽  
Vol 11 (1) ◽  
pp. 67-70 ◽  
Author(s):  
Fengzhi Dai ◽  
Tatsuya Kodani ◽  
Yutaka Fujihara
Keyword(s):  
Genetic Algorithm ◽  
Face Recognition ◽  
Recognition System ◽  
Face Recognition System

scholarly journals Face Recognition System Using Genetic Algorithm

2016 ◽  
Vol 85 ◽  
pp. 410-417 ◽  
Author(s):  
Pratibha Sukhija ◽  
Sunny Behal ◽  
Pritpal Singh
Keyword(s):  
Genetic Algorithm ◽  
Face Recognition ◽  
Recognition System ◽  
Face Recognition System

VIDEO-BASED FACE RECOGNITION AND TRACKING FROM A ROBOT COMPANION

2009 ◽  
Vol 23 (03) ◽  
pp. 591-616 ◽  
Author(s):  
T. GERMA ◽  
F. LERASLE ◽  
T. SIMON
Keyword(s):  
Genetic Algorithm ◽  
Face Recognition ◽  
Particle Filtering ◽  
Free Parameter ◽  
Principal Component ◽  
Recognition System ◽  
Support Vector ◽  
Face Recognition System ◽  
Wide Range ◽  
Video Based Face Recognition

This paper deals with video-based face recognition and tracking from a camera mounted on a mobile robot companion. All persons must be logically identified before being authorized to interact with the robot while continuous tracking is compulsory in order to estimate the person's approximate position. A first contribution relates to experiments of still-image-based face recognition methods in order to check which image projection and classifier associations give the highest performance of the face database acquired from our robot. Our approach, based on Principal Component Analysis (PCA) and Support Vector Machines (SVM) improved by genetic algorithm optimization of the free-parameters, is found to outperform conventional appearance-based holistic classifiers (eigenface and Fisherface) which are used as benchmarks. Relative performances are analyzed by means of Receiver Operator Characteristics which systematically provide optimized classifier free-parameter settings. Finally, for the SVM-based classifier, we propose a non-dominated sorting genetic algorithm to obtain optimized free-parameter settings. The second and central contribution is the design of a complete still-to-video face recognition system, dedicated to the previously identified person, which integrates face verification, as intermittent features, and shape and clothing color, as persistent cues, in a robust and probabilistically motivated way. The particle filtering framework, is well-suited to this context as it facilitates the fusion of different measurement sources. Automatic target recovery, after full occlusion or temporally disappearance from the field of view, is provided by positioning the particles according to face classification probabilities in the importance function. Moreover, the multi-cue fusion in the measurement function proves to be more reliable than any other individual cues. Evaluations on key-sequences acquired by the robot during long-term operations in crowded and continuously changing indoor environments demonstrate the robustness of the tracker against such natural settings. Mixing all these cues makes our video-based face recognition system work under a wide range of conditions encountered by the robot during its movements. The paper concludes with a discussion of possible extensions.

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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