Latent fingerprint recognition involves acquisition and comparison of latent fingerprints with an exemplar gallery of fingerprints. The diversity in the type of surface leads to different procedures to recover the latent fingerprint. The appearance of latent fingerprints vary significantly due to the development techniques, leading to large intra-class variation. Due to lack of large datasets acquired using multiple mechanisms and surfaces, existing algorithms for latent fingerprint enhancement and comparison may perform poorly. In this study, we propose a Multi-Surface Multi-Technique (MUST) Latent Fingerprint Database. The database consists of more than 16,000 latent fingerprint impressions from 120 unique classes (120 fingers from 12 participants). Including corresponding exemplar fingerprints (livescan and rolled) and extended gallery, the dataset has nearly 21,000 impressions. It has latent fingerprints acquired under 35 different scenarios and additional four subsets of exemplar prints captured using live scan sensor and inked-rolled prints. With 39 different subsets, the database illustrates intra-class variations in latent fingerprints. The database has a potential usage towards building robust algorithms for latent fingerprint enhancement, segmentation, comparison, and multi-task learning. We also provide annotations for manually marked minutiae, acquisition Pixel Per Inch (PPI), and semantic segmentation masks. We also present the experimental protocol and the baseline results for the proposed dataset. The availability of the proposed database can encourage research in handling intra-class variation in latent fingerprint recognition.

A probe face image may contain noise due to environmental conditions, incorrect use of sensors or transmission error. The performance of face recognition severely depletes when the probe image is contaminated with noise. Denoising techniques can improve recognition performance, provided the correct parameters are used. In this paper, a parameter selection framework is presented. In the proposed framework, the optimal parameter set is selected for denoising using quality assessment algorithms with low complexity. Quality score based parameter selection is evaluated on the AR face dataset. A correlation study is discussed to ascertain the relationship between the quality scores and recognition rate. The experiments suggest that the proposed framework improves the performance both in terms of accuracy and computation time.

The smart electricity distribution network, known as Smart Grid (SG), represents an optimal and intelligent framework for electricity generation and distribution, facilitated through a bidirectional information and communication network. However, setting up such an expansive network presents considerable network security challenges. Consequently, numerous threats such as impersonation attacks, repeat attacks, and man-in-the-middle attacks can pose the network with substantial difficulties. To address security and privacy concerns in SG, researchers have proposed several security protocols intending to increase protection, but exchanging information in a public channel that does not have security conditions poses serious security risks. Despite numerous security protocols having been suggested to facilitate the authentication of network entities and the generation of session keys for ensuring secure communication, some of them have security flaws or high computational overhead that makes them unsuitable for resource-constrained components. This paper presents a lightweight protocol to address the security challenges in smart grid networks. The proposed scheme incorporates a multi-level authentication mechanism and the Elliptic Curve Diffie-Hellman (ECDH) technique to ensure the secure generation of session keys. The security of the proposed protocol has been formally verified using the AVISPA simulation tool and further validated through informal analysis against various attacks. Performance evaluation results demonstrate that the protocol is computationally efficient and well-suited to the resource-constrained environment of smart grid entities. Specifically, the session key generation process is more efficient compared to several existing approaches, thereby minimizing communication delays and enhancing the overall operational performance of the system.

The growth of online services has resulted in a great need for tools to secure systems from would-be attackers without compromising the user experience. CAPTCHAs (Completely Automated Public Turing Tests to Tell Computers and Humans Apart) are one tool for this purpose, but their popular text-based form has been rendered insecure by improvements in character recognition technology. In this paper, we propose a novel imagebased CAPTCHA which employs object recognition as its test. Inspired by the negative selection approach in biological immune systems, an innovative two-phase filtering algorithm is proposed which ensures that the CAPTCHA is resilient to automated attack while remaining easy for human users to solve. In extensive testing involving over 3,000 participants, the proposed aiCAPTCHA achieved a 92.0% human success rate.