Description
The age of technologisation and the widespread use of computer and other mobile devices meant that authentication using biometric systems has received greater attention. Although biometric systems usually provides good solutions in faster authentication, convenience, greater accuracy measures, flexibility, and scalability, biometric based system of person recognition using personal characteristics to verify or identify an individual poses a challenging problem because of large variability in biometric sample quality encountered during testing and a restricted number of enrollment samples for training, the recognition performance tends to be affected over time due to changing conditions and aging of biometric data, which results in intra-class variability. Solutions in the form of adaptive biometrics which adapts the biometric reference over time have been introduced to address this issue. These adaptive biometric systems aim to adapt enrolled templates to variations in samples observed during operations. A positive feature of adaptive biometric system is that it adapts the user reference to deal with template ageing. However, despite numerous advantages, few commercial vendors have adopted auto-update procedures in their products. This is due in part to the limited under- standing and limitations associated with existing adaptation schemes.
This seminar work provides an incisive discussion on adaptive biometrics systems we are aware of, including formalization, terminology, sources or variations that motivates the use of adaptation, fundamental issues in adaptive biometric system, adaptation mechanisms, strategies, methodology, and open challenges.
CHAPTER ONE
1.0 INTRODUCTION
- Background of the study
- statement of the problem
- Aim of the study
- scope of the study
- significance of the study
CHAPTER TWO
LITERATURE REVIEW
- Literature review
CHAPTER THREE
methodology / conceptual framework
CHAPTER FOUR
4.1 Applications of Adaptive Biometric Systems.
CHAPTER FIVE
Conclusion
References
CHAPTER ONE
1.0 INTRODUCTION
1.1 BACKGROUND OF THE STUDY
The biometric technology has been lauded for it’s continual improvement, an intrinsic characteristic of this technology however noted is that a system’s error rate,( i.e., the false accept rate (FAR), false reject rate (FRR) and equal error rate (EER) (the rate at which FAR is equal to FRR), cannot attain the absolute zero. A cause of these errors is traced majorly to the compound effect of the scarcity of training samples during the enrollment phase as well as the presence of substantial sample variations due to human-sensor interaction and the acquisition environment during operations (Charu, 2015). There is also the fact, that being biological tissues in nature, biometric traits can be altered either temporarily or permanently, due to ageing (Charu, 2015), diseases or treatment to diseases.
To address this issue of reference representativeness, solutions in the form of adaptive biometrics have been introduced (Zahid et al., 2014). These adaptive biometric systems attempt to update reference galleries by integrating information captured in input operational samples.
1.2 Statement of the problem
in traditional biometric recognition systems experiences problems such inherent variability in biometric data over time due to factors like aging, environmental changes, and physiological fluctuations, which can lead to decreased accuracy; therefore, the goal of adaptive biometrics is to develop systems that can dynamically update a user’s biometric template to maintain high recognition accuracy despite these variations.
1.2 AIM OF THE STUDY
The aim of this seminar work is to carry out a study on adaptive biometric system. This work serves as a means of identifying how adaptive biometric system can be used to solve some limitations found in other biometric systems, by providing an up-to-date and complete discussion on adaptive biometrics systems we are aware of, including formalization, terminology, sources or variations that motivates the use of adaptation, adaptation strategies, evaluation methodology, and open challenges.
1.4 Significance of the study
- With this system, it saves one the pressure of collecting a large number of biometric samples during enrollment.
- It no longer becomes necessary to re-enrol or re-train the system (classifier) from scratch in order to cope up with the changing environment (Zahid et al., 2014). The convenience created can significantly reduce the cost of maintaining a biometric system.
- the actual observed variations can be incorporated into the Despite these advantages, to our knowledge, few biometric vendors such as BIOsingle (fingerprint) and Recogsys (hand geometry) have incorporated automated adaptation mechanism into their technologies at the time of this writing.
1.5 Scope of the study
The scope of this work covers the study of adaptive biometrics which is a system that that can dynamically update a user’s biometric template to maintain high recognition accuracy.
CHAPTER FIVE
Conclusion
This work has extensively discussed about adaptive biometric systems. It reiterates the fact that while Biometric authentication systems may suffer from decreasing recognition performance due to varying environmental conditions, sample modifications and template ageing, which it is argued causes intra-class variability, this work proposes that adaptive biometric authentication systems have the tendencies and the possibilities to address these deficiencies by dynamically changing their sampling and recognition processes in response to changes in the operating environment. This thesis carries weight on system efficiency and provides greater emphasis on the effective design of an adaptive biometric authentication system, with a strong requirement on the evaluation of adaptive biometric authentications with large-scale datasets to validate the feasibility of the system and its scalability in real-world with a comprehensive study of evaluation metrics.
