Face Verification results on VADANA dataset

People: Gowri Somanath, Rohith MV, Chandra Kambhamettu

Experiments for age gap [0,2]

The following table reports the accuracy at Equal Error Rate (EER), where EER is defined as the error when Correct Acceptance Rate (CAR) = Correct Rejection Rate (CRR).

CAR= (# of correctly classified intra-pairs) / (Total # of intra-pairs)

CRR= (# of correctly classified extra-pairs) / (Total # of extra-pairs)

See paper for details on experiments.

Algorithm,Dataset VADANA FGNET
Baseline: Eigenfaces (PCA+RF) 52.33 99.33
Nowak pair matching (SIFT+ERFC) Nowak 61.52 67.0 +- 2.2
Ling et. al (GOP+SVM) Ling 57.43 73.0

Code

The matlab source code used for the baseline method and our implementation of the GOP+SVM method can be found here.

References

Nowak E. Nowak and F. Jurie. Learning visual similarity measures for comparing never seen objects. In Conference on Computer Vision & Pattern Recognition, 2007.

Ling H. Ling, S. Soatto, N. Ramanathan, and D. Jacobs. Face verification across age progression using discriminative methods. Information Forensics and Security, IEEE Transactions on, 5(1):82 –91, March 2010.