Appearance invariant Entry-Exit matching using visual soft biometric traits
This addresses the challenge of reliable subject matching in surveillance for security applications, though it is incremental as it builds on existing biometric methods.
The paper tackles the problem of appearance-invariant subject recognition in entry-exit surveillance by proposing a semantic matching model that uses visual soft biometric traits like height, build, complexion, and clothing color to endorse subject exits, achieving high rank-k accuracy on benchmark datasets.
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.