Information Technology Reference
In-Depth Information
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8 training images
7 training images
6 training images
5 training images
8 training images
7 training images
6 training images
5 training images
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Rank
False Accept Rate (log scale)
(a) (b)
Fig. 8.
Experiment 3 results for our database. (a) CMC curves for 5 to 8 training
images and the corresponding (b) ROC curves.
Table 4.
Comparison of different techniques on the Yale B (10 subjects) and extended
Yale B databases. The second citation (if present) refers to the source of results.
Method
subjects Error rate on Yale B database
subset 1&2 subset 3 subset 4 total
Eigen Face w/o first 3 [13]
10
0.0
19.2
66.4
25.8
Cones-attached [9][13]
10
0.0
0.0
8.6
2.7
Harmonic Image-cast [2][13]
10
0.0
0.0
2.7
0.85
9 Points of light [13]
10
0.0
0.0
0.0
0.0
Logarithmic Total Variation [6][21]
38
0.0
1.6
1.1
-
Local Texture Features [21]
38
0.0
0.0
0.8
-
Subspace Contourlet Coeff.
38
0.0
0.0
0.0
0.0
4.4 Timing and Comparison with Other Techniques
Using a Matlab implementation on a 2.4GHz machine with 4GB RAM, the
training time using our database of 106 subjects and 6 images per subject was 2
minutes. The recognition time on the same machine and with the same gallery
size was 258 msecs. The average time required for calculating the Contourlet
transform of a face at 3 scales and 15 orientations was 100 msecs and for matching
two faces was 0.4 msecs. Table 4 shows a comparison of our algorithm to existing
techniques.
5Con lu on
We presented a novel algorithm that exploits desktop equipment for face recogni-
tion under varying illumination. We demonstrated that it is possible to construct
subspaces in the feature space for illumination invariant face recognition using
multiple images of the face under extended light source illuminations from a
computer screen. Our results on the extended Yale B and CMU-PIE databases
revealed that the subspace constructed from the Contourlet coecients [8] of 5
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