Abstract This paper proposes a novel algorithm for computing discriminative descriptors named as a sparse coded composite descriptor (SCCD) for robust human activity recognition. The proposed method blends the state‐of‐the‐art handcrafted features and the discriminative nature of the sparse representation of visual information. The human activity is firstly modelled using any handcrafted feature, and then the sparse codes computed on a discriminative sparse dictionary of these features are embedded to provide discrimination in the feature set. Finally, a support vector machine (SVM) is trained using the proposed SCCDs to perform classification of different human activities. A new feature named as differential motion descriptor (DMD) is also proposed to extract the motion as well as spatial information from an activity video. The simulation results reveal that in comparison with the handcrafted feature, the corresponding SCCD improves the recognition accuracy significantly. The proposed method is compared with state‐of‐the‐art methods on KTH, Ballet, UCF50, and HMDB51 datasets and the proposed methodology of composite features outperforms these methods in terms of recognition accuracy.

Multimodal biometric systems provide prospective advantages over traditional unimodal systems and are employed diversely for numerous applications. However, issues of data privacy and identity-theft emerges with the extensive use of these systems. To address these issues we propose a multimodal cancelable biometric system based on realtime Deep Feature Unification (DFU). For this, keys-based generic feature extraction is introduced to achieve revocability and dimensionality reduction. Non-invertibility is obtained through random projection of Key Deep features to Query Deep features. Proposed adaptive graph-based fusion process not only extracts complementary information across multiple modalities but also generates multimodal Unified template. The cross diffusion of normalized and optimal graphs ensure the unlinkability and robustness to dynamic environment. Proposed biometric system is assessed over benchmark datasets and shows promising performance against state-of-the-art methods. Average DI and EER achieved by proposed method are 10.35 and 0.12, respectively. Further, robustness against adversary attacks is demonstrated.

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.

The aim of this paper is to give an overview of state-of-the-art human detection methods, classify them into reasonable categories and listing new trends in this field. Human detection in static images or video sequences is a challenging area of research with its main application in surveillance, defence, intelligent vehicles and robotics. In this survey paper, we classify the human detection methods based upon the mode of data acquisition and features used for detection of human. The review of different algorithms reported in last few years for human detection is tabulated. Also, we reviewed standard dataset for evaluation of human detection and studied the statistics about these dataset such as number of frames, challenging environments conditions, ground truth structure, camera details, etc. Different performance measures for human detection algorithms both qualitative and quantitative are listed out. Our survey gauges the gap between the present research and future requirements.