Comparative Study of Traditional Techniques for Unsupervised Autonomous Intruder Detection.

In this paper we investigate five traditional techniques to extract features within a face image, and we evaluate them by applying the Kernel-based Online Anomaly Detection (KOAD) algorithm. The main objective of this work is to explore the various fundamental feature extraction techniques that can be used to identify whether a person’s face is covered by a mask or not. Although face covering or wearing a mask is recommended during this global COVID-19 pandemic, deliberate face occlusion is considered to be a suspicious activity in a normal scenario. Even during this pandemic, it may be considered suspicious if someone is covering his/her face inside an ATM booth or an apartment complex during odd hours, for instance. Our proposed framework detects such intrusion activity by combining a traditional face detection algorithm with KOAD. Comparative analysis is performed for each filter used and we show that our proposed system achieves high detection accuracy with low computational complexity, while also providing the added benefits of being adaptive, portable, and involving low infrastructural costs.