Intruder recognition in uncontrolled environments is a critical function of biometric-based security control systems (SCS), ensuring protection at facilities with large crowds. The accuracy of such systems can be improved by combining multiple biometric traits extracted from video imagery. However, processing video data involves several challenges, including viewpoint variation, occlusion, and selecting an appropriate feature fusion level. To resolve these challenges, a complete approach to intruder recognition is proposed. The approach is based on video preprocessing, the combination of two convolutional neural networks (CNNs), Dempster–Shafer theory, and the random forest method. These techniques fuse behavioral biometric features at the score level to classify video sequences. Gait and gestural behavior are selected as biometric modalities, as they can be captured without direct subject interaction. For the classification task, three classes of video data are defined: class 1 – person is not intruder, class 2 – a potential intruder, class 3 – intruder. The study also presents a general architecture for the proposed approach, along with a detailed description of its processing stages. The effectiveness of the approach is measured through experiments performed on the KTH dataset, which comprises six types of simple human actions performed by different subjects under different background conditions. Experimental results show that the proposed approach improves intruder recognition accuracy in uncontrolled environments, achieving an 87 % classification rate.
