Research Article

# A Gabor-Block-Based Kernel Discriminative Common Vector Approach Using Cosine Kernels for Human Face Recognition

## Table 2

Specificity and sensitivity measure of the FRAV2D dataset.
 Total no. of classes = 100, total no. of images = 1800 Individual belonging to a particular class Using first 3 images of an individual as training images Positive Negative FRAV2D test Positive Negative Using first 4 images of an individual as training images Positive Negative FRAV2D test Positive Negative
So considering the first 4 images in Figures 2(a)2(d) of a particular individual for training the achieved rates are as follows.
False positive rate = /( + ) = 1 − Specificity = .3%.
False negative rate = /( + ) = 1 – Sensitivity = 3.15%.
Accuracy = ( + )/( + + + ) 98.3%.
So considering the first 3 images in Figures 2(a)2(c) of a particular individual for training the achieved rates are as follows.
False positive rate = /( + ) = 1 − Specificity = 1%.
False negative rate = /( + ) = 1 – Sensitivity = 5.125%.
Accuracy = ( + )/( + + + ) 96.9%.