scholarly journals C# solutions for a face detection and recognition system

2007 ◽  
Vol 20 (1) ◽  
pp. 93-105
Author(s):  
Catalin-Daniel Caleanu ◽  
Corina Botoca
Keyword(s):  
Neural Network ◽  
Face Detection ◽  
Object Oriented ◽  
Recognition System ◽  
Video Stream ◽  
Recognition Phase ◽  
Key Issues ◽  
Face Detection And Recognition ◽  
Artificial Neural Network Ann ◽  
Detection And Recognition

Key issues on using a new programming language - C# - in implementation of a face detection and recognition (FDR) system are presented. Mainly the following aspects are detailed: how to acquire an image, broadcast a video stream, manipulate a database, and finally, the detection/recognition phase all in relation with theirs possible C#/.NET solutions. Emphasis was placed on artificial neural network (ANN) methods for face detection/recognition along with C# object oriented implementation proposal.

A Near-Infrared Face Detection and Recognition System Using ASM and PCA+LDA

2014 ◽  
Vol 9 (10) ◽  
Author(s):  
Peiyi Shen ◽  
Liang Zhang ◽  
Juan Song ◽  
Hu Xu ◽  
Lianjie Qin ◽  
...  
Keyword(s):  
Face Detection ◽  
Near Infrared ◽  
Recognition System ◽  
Face Detection And Recognition ◽  
Detection And Recognition

scholarly journals FaceID:a face detection and recognition system

1997 ◽  
Author(s):  
Manesh B. Shah ◽  
Nageswara S. V. Rao ◽  
Victor Olman ◽  
Edward C. Uberbacher ◽  
Reinhold C. Mann
Keyword(s):  
Face Detection ◽  
Recognition System ◽  
Face Detection And Recognition ◽  
Detection And Recognition

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%.

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