Visual tracking is a complex problem due to unconstrained appearance variations and a dynamic environment. The extraction of complementary information from the object environment via multiple features and adaption to the target's appearance variations are the key problems of this paper. To this end, we propose a robust object tracking framework based on the unified graph fusion (UGF) of multicue to adapt to the object's appearance. The proposed cross-diffusion of sparse and dense features not only suppresses the individual feature deficiencies but also extracts the complementary information from multicue. This iterative process builds robust unified features which are invariant to object deformations, fast motion, and occlusion. Robustness of the unified feature also enables the random forest classifier to precisely distinguish the foreground from the background, adding resilience to background clutter. In addition, we present a novel kernel-based adaptation strategy using outlier detection and a transductive reliability metric. The adaptation strategy updates the appearance model to accommodate variations in scale, illumination, and rotation. Both qualitative and quantitative analyses on benchmark video sequences from OTB-50, OTB-100, VOT2017/18, and UAV123 show that the proposed UGF tracker performs favorably against 18 other state-of-the-art trackers under various object tracking challenges.

Multimodal biometric systems offer numerous advantages over unimodal counterparts and are being used extensively in diverse applications. However, fusion of biometric data is a non-trivial task and curtail employability of multimodal systems for a varying set of biometric characteristics with different type and dimension. Moreover, comprehensive solutions against adversary attacks that ensure template protection and prevent presentation attacks are not in place. In this article, a secure multimodal cancelable biometric system is proposed to address these concerns. This approach introduces key images based generic feature extraction technique which reduces feature dimension and achieves revocability. The non-invertibility and unlinkability are ensured through cross-diffusion of complementary information from different modalities. A new feature fusion method based on an adaptive graph is proposed to generate multimodal cancelable biometric templates. Robustness against presentation attack is accomplished through quality based adaptation of features. Extensive experimentation is performed on benchmark databases for fingerprint, face, and iris, to illustrate the efficacy of multimodal cancelable templates. The proposed approach is shown to perform favorably against state-of-the-art feature fusion methods. Furthermore, the resilience of the proposed approach against security and privacy attacks is demonstrated.