Description
ABSTRACT
Biometric based person recognition poses a challenging problem because of large variability in biometric sample quality encountered during testing and a restricted number of enrollment samples for training. Solutions in the form of adaptive biometrics have been introduced to address this issue. These adaptive biometric systems aim to adapt enrolled templates to variations in samples observed during operations. 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.
ABSTRACT
CHAPTER ONE
1.0 INTRODUCTION
- Background of the study
- Aim of the study
- Advantages of adaptive biometric system
CHAPTER TWO
LITERATURE REVIEW
- Review Of The Study
CHAPTER THREE
- Fundamental issues behind adaptive biometric systems
- Adaptation mechanism
- Conclusion
References
CHAPTER ONE
1.0 INTRODUCTION
1.1 BACKGROUND OF THE STUDY
While the biometric technology continues to improve, an intrinsic characteristic of this technology is that a system’s error rate, e.g., 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 major cause of these errors is 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). Apart from this, being biological tissues in nature, biometric traits can be altered either temporarily or permanently, due to ageing (Charu, 2015), diseases or treatment to diseases.
Solutions in the form of adaptive biometrics have been introduced to address this issue of reference representativeness (Zahid et al., 2014). These adaptive biometric systems attempt to update reference galleries by integrating information captured in input operational samples. The two-fold aim is to continuously adapt the biometric system to the intra- class variation of the input data as a result of :
- changing acquisition conditions that may have adverse impact on the system, g., pose and illumination changes for face biometrics, and
- age and life-style related changes that can cause permanent changes to the biometric
Most of the existing automated adaptive biometric systems have adopted semi-supervised learning (Zahid et al., 2014) for the purpose of adaptation. Semi-supervised learning is a machine learning scheme based on the joint use of labeled and unlabeled samples. In other words, input samples are assigned identity labels using enrolled references and the reference set with the newly classified input samples. The efficacy of the system can be gauged by comparing the obtained performance gain with a traditional biometric system which does not have any adaptation mechanism. The expected performance gain is dependent on the effective labeling (classification) of the input samples. This is because misclassification errors will introduce impostor samples into the updated reference set, the result of which can be counterproductive.
An adaptive biometric system may also operate in supervised mode in which biometric samples are manually labeled (Zahid et al., 2014). The supervised method represents the best case performance as all the available positive (genuine) samples are used for adaptation. However, manual intervention may be time consuming and costly. Therefore, it is generally infeasible to manually update references regularly.
1.2 AIM OF THE STUDY
The aim of this seminar is to carry out a study on adaptive biometric system. This seminar will serve as a means of identifying how adaptive biometric system can be used to solve some limitations found in other biometric systems.
1.3 ADVANTAGES OF ADAPTIVE BIOMETRIC SYSTEM
An adaptive biometric system has numerous advantages such as:
- With this system, one no longer needs to collect a large number of biometric samples during enrollment.
- It is no longer 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). This convenience 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.
- Conclusion
In this seminar, we have extensively discussed about adaptive biometric. Biometric authentication systems may suffer from decreasing recognition performance due to varying environmental conditions and sample ageing, which cause intra-class variability. Adaptive biometric authentication systems have been proposed to address these deficiencies by dynamically changing their sampling and recognition processes in response to changes in the operating environment. This seminar provides the complete discussion on the design of an adaptive biometric authentication system. In addition to this, the evaluation of adaptive biometric authentications with large-scale datasets is required to validate the feasibility of the system and its scalability in real-world with a comprehensive study of evaluation metrics
