  The problem of appearance invariant subject recognition for Entry-Exit
surveillance applications is addressed. A novel Semantic Entry-Exit matching
model that makes use of ancillary information about subjects such as height,
build, complexion and clothing color to endorse exit of every subject who had
entered private area is proposed in this paper. The proposed method is robust
to variations in clothing. Each describing attribute is given equal weight
while computing the matching score and hence the proposed model achieves high
rank-k accuracy on benchmark datasets. The soft biometric traits used as a
combination though cannot achieve high rank-1 accuracy, it helps to narrow down
the search to match using reliable biometric traits such as gait and face whose
learning and matching time is costlier when compared to the visual soft
biometrics.
