Global soft biometrics in surveillance: benchmark in the field & open challenges

Global soft biometrics-based identification refers to the recognition of subjects using human traits such as gender, age, and ethnicity. Unlike traditional biometric methods that rely on unique physical markers such as fingerprints or iris scans, soft biometrics represents a non-intrusive, viable, and versatile approach, thus making them particularly valuable for surveillance and security applications. Despite significant advances, several issues have been associated with traditional biometrics, like maintaining accuracy, addressing algorithmic bias, and limited computational efficiency. To address those issues, this paper presents a comprehensive coverage of the current advances in Global soft biometric-based recognition as a solution, where four key contributions are made; i.e., (i) advocacy on the relevance and impact of soft biometrics in surveillance and security, (ii) development of a new and unique CeleBImg dataset to overcome algorithmic biases and improve diversity in soft biometric-based recognition, (iii) rigorous performance comparison of current methods in-practice for Global soft biometrics-based recognition and, (iv) identification of open challenges with potential solutions in the field within the context of surveillance and security. This paper sets a solid foundation for using Global soft biometrics in the CCTV-based surveillance and security domain, with their significance, relevance and effectiveness.