IWBF 2014
2nd International Workshop on Biometrics and Forensics
27‐28th March, Valletta, Malta
TOWARDS PRACTICAL SPACE‐VARIANT BASED FACE RECOGNITION AND AUTHENTICATION
Enrico Grosso, Andrea Lagorio, Luca Pulina, and Massimo Tistarelli
Computer Vision Laboratory - University of Sassari, ITALY
CONTEXT AND MOTIVATION
Combining the extraction of salient points and space‐variant
descriptors, a novel and efficient method for face recognition and
authentication can be implemented. This method is called BSV.
Experimental results demonstrate that this approach is
computationally feasible and guarantees a significant increase in
accuracy with respect to the original SIFT approach.
THE BOOSTED SPACE VARIANT FRAMEWORK
BSV(img1, img2)
1 kp1 = SIFTDETECT(img1)
2 kp2 = SIFTDETECT(img2)
3 desc1 = SIFTEXTRACT(kp1)
4 desc2 = SIFTEXTRACT(kp2)
5 skp1 = NIL
6 skp2 = NIL
7 SIFTMATCHER(desc1, desc2, skp1, skp2)
8 score = ‐1
9 for i = 0 to SIZE(skp1)
10 currScore = LPCORR(skp1[i], skp2[i])
11 if currScore > score then
12 score = currScore
13 return score
Example of a single space variant filter centered on a generic point
RESULTS
FACE RECOGNITION (FERET DATABASE)
Accuracy
BSV
GSIFT
SIFT
82%
78%
72%
FACE AUTHENTICATION (BANCA DATABASE)
MC
UD
UA
P
MC
UD
UA
P
BSV
7.12%
23.22% 19.43%
23.31%
BSV
3.73%
24.64% 19.00%
21.45%
GSIFT
7.02%
12.43% 18.32%
18.09%
GSIFT
2.00%
12.82% 17.18%
15.55%
SIFT
12.12%
39.44% 28.37%
33.72%
SIFT
7.82%
42.18% 29.55%
38.91%
WER (1)
WER (0.1)
MC
UD
UA
P
BSV
10.27%
21.36% 19.00%
25.54%
GSIFT
11.00%
10.36% 18.82%
20.45%
SIFT
15.18%
34.82% 34.45%
29.09%
WER (10)