Abstract
Person search based on semantic attributes description presents an interest task for intelligent video surveillance applications. The main objective is to locate a suspect or to find a missing person in public areas using a semantic description (e.g. a 40-year-old asian woman) provided by an eyewitness. Such a description provides the facial soft biometric related to the facial semantic attributes (i.e. age, gender and ethnicity). In this paper, we introduced a new approach for person search named “Quick-Search” based on a facial semantic attributes description to enhance the person search task in an unconstrained environment. The main contribution of the paper is to introduce a multi-attributes score fusion method which relies on soft biometric features (age, gender, ethnicity) to improve the person search in a large dataset. An experimental study was conducted on the challenging FairFace dataset to validate the effectiveness of the proposed person search approach.
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Dammak, S., Mliki, H., Fendri, E. (2023). Person Quick-Search Approach Based on a Facial Semantic Attributes Description. In: Blanc-Talon, J., Delmas, P., Philips, W., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2023. Lecture Notes in Computer Science, vol 14124. Springer, Cham. https://doi.org/10.1007/978-3-031-45382-3_7
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