Incremental Real Time Support Vector Machines

2018 ◽  
pp. 221-230
Author(s):  
Fahmi Ben Rejab ◽  
Kaouther Nouira
Keyword(s):  
Support Vector Machines ◽  
Real Time ◽  
Support Vector ◽  
Vector Machines

FACE RECOGNITION USING SUPPORT VECTOR MACHINES WITH LOCAL CORRELATION KERNELS

2002 ◽  
Vol 16 (01) ◽  
pp. 97-111 ◽  
Author(s):  
KWANG IN KIM ◽  
JIN HYUNG KIM ◽  
KEECHUL JUNG
Keyword(s):  
Face Recognition ◽  
Support Vector Machines ◽  
Real Time ◽  
Recognition Rate ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Local Correlation ◽  
Correlation Kernel ◽  
Vector Machines ◽  
The Face

This paper presents a real-time face recognition system. For the system to be real time, no external time-consuming feature extraction method is used, rather the gray-level values of the raw pixels that make up the face pattern are fed directly to the recognizer. In order to absorb the resulting high dimensionality of the input space, support vector machines (SVMs), which are known to work well even in high-dimensional space, are used as the face recognizer. Furthermore, a modified form of polynomial kernel (local correlation kernel) is utilized to take account of prior knowledge about facial structures and is used as the alternative feature extractor. Since SVMs were originally developed for two-class classification, their basic scheme is extended for multiface recognition by adopting one-per-class decomposition. In order to make a final classification from several one-per-class SVM outputs, a neural network (NN) is used as the arbitrator. Experiments with ORL database show a recognition rate of 97.9% and speed of 0.22 seconds per face with 40 classes.

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