Structural Feature Based Anomaly Detection for Packed Executable Identification - Computational Intelligence in Security for Information Systems - page 306

1

∑

r 1

p = isequal (arg max ( SIMILARITY ), j )

(14)

r 1 i , j

299

i

j ∈ { ,... 300 } − { VI }

z

where isequal ( x,y ) is defined as equation 15.

1 x == y

⎩ ⎨ ⎧

(15)

isequal ( x , y ) =

0 otherwise

6 Experimental Study

Experimental results are reported over 300 pairs of images. Each pair of images
belongs to an employee (personnel) of our laboratory. All the images have the same
resolution. All of them are first equalized using equalizing their histograms.
Live-one-out technique is used to test ensemble classifier over these images. Also
features of 5 different frequencies and 8 orientations are extracted. So, there are forty
similarity matrices. 599 images, except in weighted majority voting, are used as
training set because there is no longer need to validation set. It is worthy to mention
that the best classifier using only one of the similarity matrix, has just 76.63%
recognition ratio. While recognition ratio of classifier mentioned above has 90.17%
recognition ratio with majority voting, by use of the average voting as final results the
89.32% recognition ratio is achieved. But the combinational proposed approach has
92.67% recognition ratio. The Table 1 summarizes the results.

Table 1. Face recognition ratios of different methods

Best C f MV(C f ) MAV(C f )

WMAV

76.63 89.32 90.17 92.67

7 Conclusions

In this paper, new face identification algorithm is proposed. In the proposed algorithm
first gabor wavelet features with different frequencies are extracted maximizing over
different orientations. Defining one classifier per each frequency an ensemble is
obtained. The ensemble uses weighted majority average voting as the consensus
function. It is shown that the proposed mechanism works well in an employees'
repository of laboratory.

References

1. Ross, A., Jain, A.K.: Information fusion in biometrics. Pattern Recognition Letters 24(13),
2115-2125 (2003)
2. Pekalska, E., Duin, R., Skurichina, M.: A discussion on the classifier projection space
for classifier combining. In: Roli, F., Kittler, J. (eds.) MCS 2002. LNCS, vol. 2364,
pp. 137-148. Springer, Heidelberg (2002)