Effective Face Feature For Human Identification

Face image is one of the most important parts of human body. It is easily use for identification process. People naturally identify one another through face images. Due to increase rate of insecurity in our society, accurate machine based face recognition systems are needed to detect impersonators. Face recognition systems comprise of face detector module, preprocessing unit, feature extraction subsystem and classification stage. Robust feature extraction algorithm plays major role in determining the accuracy of intelligent systems that involves image processing analysis. In this paper, pose invariant feature is extracted from human faces. The proposed feature extraction method involves decomposition of captured face image into four sub-bands using Haar wavelet transform thereafter shape and texture features are extracted from approximation and detailed bands respectively. The pose invariant feature vector is computed by fusing the extracted features. Effectiveness of the feature vector in terms of intra-person variation and inter-persons variation was obtained from feature plot

Robust Palm-Print Feature for Human Identification

Palm-print is a unique biometric trait commonly uses to distinguish people. Identification of people with aid of machine is needed to solve insecurity challenges in our society. Human palm-print is a good raw material for machine based identification systems. These systems require strong predominate feature from palm-print for successful operation. In this work, a discriminate feature that can be used to differentiate people accurately is extracted from palm-print image. Edge detected palm-print image is sliced into smaller image blocks through centre points thereafter robust feature vector is generated from these smaller image blocks. The new feature was experimental using feature plot and it is shown clearly that this feature will deliver excellent classification result

Development of a Recognition Algorithm for Newborn and Infant Fingerprints

This research seeks to develop a fingerprint recognition algorithm with emphasis on feature extraction for the automatic recognition of newborns and infants. The feature extraction algorithm will extract the salient features required for identification/verification of the identity of newborns and infants from the fingerprint images captured with the ubiquitous 500ppi fingerprint scanners. It is expected to produce a database of fingerprints acquired from newborns and infants and an automated fingerprint recognition system for the identification of newborns and infants with improved speed and accuracy, cost reduction and the least possible vulnerability to spoofing

Biometric Enabled E-Banking in Nigeria: Management and Customers’ Perspectives

The adoption of biometric technology is rapidly increasing around the globe due to the increasing sensitivity of security issues. With the recent 2015-plan and collaborations of the Central Bank of Nigeria to incorporate biometrics into her banking system, it is imperative to assess the perception of the managers and customers to the use of the long-awaited biometrics for secure, seamless and successful transactions.  The banking sector touches the daily lives of at least 60% of the over 150-million Nigerian population and it is expected to increase as more security measures are put in place.  Therefore, this empirical evaluation captures the factors influencing the perception of the bank management and ATM users. A total of 740 respondents participated in the survey cutting across different age groups and educational backgrounds. Descriptive statistics and T-test analysis of the survey showed that management and customers of strongly support the adoption of biometric ATM in Nigeria. Keywords: E-Banking, Biometric, ATM,  Security

Biometric Enabled E-Banking in Nigeria: Management and Customers’ Perspectives

The adoption of biometric technology is rapidly increasing around the globe due to the increasing sensitivity of security issues. With the recent 2015-plan and collaborations of the Central Bank of Nigeria to incorporate biometrics into her banking system, it is imperative to assess the perception of the managers and customers to the use of the long-awaited biometrics for secure, seamless and successful transactions. The banking sector touches the daily lives of at least 60% of the over 150-million Nigerian population and it is expected to increase as more security measures are put in place. Therefore, this empirical evaluation captures the factors influencing the perception of the bank management and ATM users. A total of 740 respondents participated in the survey cutting across different age groups and educational backgrounds. Descriptive statistics and T-test analysis of the survey showed that management and customers of strongly support the adoption of biometric ATM in Nigeria

Integrating Automated Fingerprint-based Attendance into a University Portal System

Student learning and academic performance hinge largely on frequency of class attendance and participation. The fingerprint recognition system aims at providing an accurate and efficient attendance management service to staff and students within an existing portal system. The integration of a unique and accurate identification system into the existing portal system offers at least, two advantages: accurate and efficient analysis and reporting of student attendance on a continuous basis; and also facilitating the provision of personalized services, enhancing user experience altogether. An integrated portal system was developed to automate attendance management and tested for fifty students in five attempts. The 98% accuracy achieved by the system points to the feasibility of large scale deployment and interoperability of multiple devices using existing technology infrastructure

Multi-instance contingent fusion for the verification of infant fingerprints

It is imperative to establish an automated system for the identification of neonates (1–28 days old) and infants (29 days–12 months old) through the utilisation of the readily accessible 500 ppi fingerprint reader. This measure is crucial in addressing the issue of newborn swapping, facilitating the identification of missing children, monitoring immunisation records, maintaining comprehensive medical history, and other related purposes. The objective of this study is to demonstrate the potential for future identification of infants using fingerprints obtained from a 500 ppi fingerprint reader by employing a fusion technique that combines multiple instances of fingerprints, specifically the left thumb and right index fingers. The fingerprints were acquired from babies who were between the ages of one day and six months at the enrolment session. The sum-score fusion algorithm was implemented. The approach mentioned above yielded verification accuracies of 73.8%, 69.05%, and 57.14% for time intervals of 1 month, 3 months, and 6 months, respectively, between the enrolment and query fingerprints

DEVELOPMENT OF A MULTI-INSTANCE CONTINGENT FUSION ALGORITHM FOR THE VERIFICATION OF INFANT FINGERPRINTS

Birth registration is a fundamental right for children, but approximately 237 million children below the age of 5 lack proper documentation, making them vulnerable to identity theft, newborn swapping, and child abduction. Traditional birth certificates are not reliable as they can be falsified or stolen. To address this issue, biometric birth registration, specifically using fingerprints, offers a digital identity that can last a lifetime. While other biometric traits like face, iris, palmprint, and footprint have been explored, fingerprints are the most widely accepted due to their ubiquity, ease of acquisition and widespread acceptance. However, challenges in infant fingerprint recognition include intra-class variation, the need for robust algorithms for low-resolution fingerprint images, and a lack of publicly available demographic infant fingerprint datasets. Therefore, this study aims to create a relevant dataset of infant fingerprints and develop a multi-instance contingent fusion algorithm to verify these fingerprints. The study involved obtaining fingerprints from 250 infants aged 1 day to 10 months using a fingerprint reader with a resolution of 500 ppi. The acquired fingerprints were pre-processed, and minutiae features were extracted using the MINDTCT algorithm. The extracted features of the enrolment and query fingerprints were compared using the BOZORTH3 matching algorithm, and a match score was obtained. This match score was compared to a threshold, with scores below the threshold resulting in the rejection of the infant's identity and scores above the threshold accepting it. The multi-instance contingent fusion algorithm was developed to accommodate situations where a baby's identity cannot be verified with one finger. It allows for verifying the baby's identity using a second finger. If both fingers fail to verify the identity, the match scores from both fingers are fused and compared to a predetermined threshold. The infant's identity is considered genuine if the fused score surpasses the threshold. Conversely, the baby's identity is only denied if the fused score falls below the threshold. The uniqueness of contingent fusion is that the match scores are only fused when neither of the two fingers can verify the infant's identity, thereby reducing computational complexity. The results show that for infants between 0 – 3 months old at the time of enrolment, without the multi-instance contingent fusion algorithm, the system generated verification accuracies of 34.1%, 35.71% and 11.9% for time-lapses of 1 month, 3 months and 6 months respectively, between enrolment and query fingerprints while the multi-instance contingent fusion algorithm generated verification accuracies of 73.8%, 69.05% and 57.14% for time lapses of 1 month, 3 months and 6 months respectively, between enrolment and query fingerprints. In conclusion, a dataset of infant fingerprints with a resolution of 500 ppi was developed, and the identities of babies older than 6 months were successfully verified with the fingerprint images acquired when they were younger than 6 months by employing the developed multi-instance contingent fusion algorithm. Longitudinal acquisition of infant fingerprint images and the inclusion of ancillary information, like gender and ethnicity, are therefore recommended to improve the accuracy of the verification system