An enhanced fuzzy commitment scheme in biometric template protection

Biometric template protection consists of two approaches; Feature Transformation (FT) and Biometric Cryptography (BC). This research focuses on Key-Binding Technique based on Fuzzy Commitment Scheme (FCS) under BC approach. In FCS, the helper data should not disclose any information about the biometric data. However, literatures showed that it had dependency issue in its helper data which jeopardize security and privacy. Moreover, this also increases the probability of privacy leakage which lead to attacks such as brute-force and cross-matching attack. Thus, the aim of this research is to reduce the dependency of helper data that can caused privacy leakage. Three objectives have been set such as (1) to identify the factors that cause dependency on biometric features (2) to enhance FCS by proposing an approach that reduces this dependency, and (3) to evaluate the proposed approach based on parameters such as security, privacy, and biometric performance. This research involved four phases. Phase one, involved research review and analysis, followed by designing conceptual model and algorithm development in phase two and three respectively. Phase four, involved with the evaluation of the proposed approach. The security and privacy analysis shows that with the additional hash function, it is difficult for adversary to perform brute‐force attack on information stored in database. Furthermore, the proposed approach has enhanced the aspect of unlinkability and prevents cross-matching attack. The proposed approach has achieved high accuracy of 95.31% with Equal Error Rate (EER) of 1.54% which performs slightly better by 1.42% compared to the existing approach. This research has contributed towards the key-binding technique of biometric fingerprint template protection, based on FCS. In particular, this research was designed to create a secret binary feature that can be used in other state-of-the-art cryptographic systems by using an appropriate error-correcting approach that meets security standards

Malaysian medicinal plant leaf shape identification and classification

Malaysian medicinal plants may be abundant natural resources but there has not been much research done on preserving the knowledge of these medicinal plants which enables general public to know the leaf using computing capability.This study proposes a framework to identify and classify tropical medicinal plants in Malaysia based the extracted patterns from the leaf.The extracted patterns from medicinal plant leaf are obtained based on several angle features.Five classifiers, obtained from WEKA and an ensemble classifier, called Direct Ensemble Classifier for Imbalanced Multiclass Learning (DECIML), are used to compare their performance accuracies over this data.In this experiment, five species of Malaysian medicinal plants are identified and classified in which each species will be represented by using 65 images.This study is important in order to assist local community to utilize the knowledge discovery and application of Malaysian medicinal plants for future generation

Security, Comfort, Healthcare, and Energy Saving: A Review on Biometric Factors for Smart Home Environment

The Internet of Things (IoT) have become significantly important in authentication mechanisms in which traditional authentication have shift to the biometric factors whereby biometric is said to offer more security and convenience to the users.The purpose of this paper is to provide an extensive review on biometric factors for smart home environments that are intended for security, comfort, healthcare, and energy saving.This paper also discusses the security authentication mechanisms, which are knowledge factor (password, PIN), ownership factor (ID card, passport), and inherent factor (fingerprint, iris, facial), known as biometric factors.Biometric factors can be used as authentications for smart home environments, which are more robust and reliable in terms of accuracy, convenience, and speed