Abstract
The problem of appearance invariant subject re-identification 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 appearances such as clothing, carrying, and head masking. 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 soft biometrics.
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This work has been supported by The University Grants Commission, India.
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Vinay Kumar, V., Nagabhushan, P. (2021). Appearance-Invariant Entry-Exit Matching Using Visual Soft Biometric Traits. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds) Computer Vision and Image Processing. CVIP 2020. Communications in Computer and Information Science, vol 1376. Springer, Singapore. https://doi.org/10.1007/978-981-16-1086-8_23
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