Research Article

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

## Table 4

Specificity and sensitivity measure of the FERET dataset.
 Total no. of classes = 200, Total no. of images = 3600 Individual belonging to a particular class Using first 3 images of an individual as training images Positive Negative Positive FERET test Negative Using first 4 images of an individual as training images Positive Negative Positive FERET test Negative
So considering the first 4 images in Figures 4(a)4(d) of a particular individual for training the achieved rates are:
False positive rate = /( + ) = 1 − Specificity = .72%.
False negative rate = /( + ) = 1 – Sensitivity = 6%.
Accuracy = ( + )/( + + + ) 96.6%.
So considering in the first 3 images Figures 4(a)4(c) of a particular individual for training the achieved rates are:
False positive rate = /( + ) = 1 − Specificity = 1.2%.
False negative rate = /( + ) = 1 – Sensitivity = 6.75%.
Accuracy = ( + )/( + + + ) 96.1%.