Genetic Programming for Multibiometrics

Biometric systems suffer from some drawbacks: a biometric system can provide in general good performances except with some individuals as its performance depends highly on the quality of the capture. One solution to solve some of these problems is to use multibiometrics where different biometric systems are combined together (multiple captures of the same biometric modality, multiple feature extraction algorithms, multiple biometric modalities...). In this paper, we are interested in score level fusion functions application (i.e., we use a multibiometric authentication scheme which accept or deny the claimant for using an application). In the state of the art, the weighted sum of scores (which is a linear classifier) and the use of an SVM (which is a non linear classifier) provided by different biometric systems provide one of the best performances. We present a new method based on the use of genetic programming giving similar or better performances (depending on the complexity of the database). We derive a score fusion function by assembling some classical primitives functions (+, *, -, ...). We have validated the proposed method on three significant biometric benchmark datasets from the state of the art

eBiometrics: an enhanced multi-biometrics authentication technique for real-time remote applications on mobile devices

The use of mobile communication devices with advance sensors is growing rapidly. These sensors are enabling functions such as Image capture, Location applications, and Biometric authentication such as Fingerprint verification and Face & Handwritten signature recognition. Such ubiquitous devices are essential tools in today's global economic activities enabling anywhere-anytime financial and business transactions. Cryptographic functions and biometric-based authentication can enhance the security and confidentiality of mobile transactions. Using Biometric template security techniques in real-time biometric-based authentication are key factors for successful identity verification solutions, but are venerable to determined attacks by both fraudulent software and hardware. The EU-funded SecurePhone project has designed and implemented a multimodal biometric user authentication system on a prototype mobile communication device. However, various implementations of this project have resulted in long verification times or reduced accuracy and/or security. This paper proposes to use built-in-self-test techniques to ensure no tampering has taken place on the verification process prior to performing the actual biometric authentication. These techniques utilises the user personal identification number as a seed to generate a unique signature. This signature is then used to test the integrity of the verification process. Also, this study proposes the use of a combination of biometric modalities to provide application specific authentication in a secure environment, thus achieving optimum security level with effective processing time. I.e. to ensure that the necessary authentication steps and algorithms running on the mobile device application processor can not be undermined or modified by an imposter to get unauthorized access to the secure system

Recent Application in Biometrics

In the recent years, a number of recognition and authentication systems based on biometric measurements have been proposed. Algorithms and sensors have been developed to acquire and process many different biometric traits. Moreover, the biometric technology is being used in novel ways, with potential commercial and practical implications to our daily activities. The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in biometrics. The topics covered in this book reflect well both aspects of development. They include biometric sample quality, privacy preserving and cancellable biometrics, contactless biometrics, novel and unconventional biometrics, and the technical challenges in implementing the technology in portable devices. The book consists of 15 chapters. It is divided into four sections, namely, biometric applications on mobile platforms, cancelable biometrics, biometric encryption, and other applications. The book was reviewed by editors Dr. Jucheng Yang and Dr. Norman Poh. We deeply appreciate the efforts of our guest editors: Dr. Girija Chetty, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park and Dr. Sook Yoon, as well as a number of anonymous reviewers

Smart aging : utilisation of machine learning and the Internet of Things for independent living

Smart aging utilises innovative approaches and technology to improve older adults’ quality of life, increasing their prospects of living independently. One of the major concerns the older adults to live independently is “serious fall”, as almost a third of people aged over 65 having a fall each year. Dementia, affecting nearly 9% of the same age group, poses another significant issue that needs to be identified as early as possible. Existing fall detection systems from the wearable sensors generate many false alarms; hence, a more accurate and secure system is necessary. Furthermore, there is a considerable gap to identify the onset of cognitive impairment using remote monitoring for self-assisted seniors living in their residences. Applying biometric security improves older adults’ confidence in using IoT and makes it easier for them to benefit from smart aging. Several publicly available datasets are pre-processed to extract distinctive features to address fall detection shortcomings, identify the onset of dementia system, and enable biometric security to wearable sensors. These key features are used with novel machine learning algorithms to train models for the fall detection system, identifying the onset of dementia system, and biometric authentication system. Applying a quantitative approach, these models are tested and analysed from the test dataset. The fall detection approach proposed in this work, in multimodal mode, can achieve an accuracy of 99% to detect a fall. Additionally, using 13 selected features, a system for detecting early signs of dementia is developed. This system has achieved an accuracy rate of 93% to identify a cognitive decline in the older adult, using only some selected aspects of their daily activities. Furthermore, the ML-based biometric authentication system uses physiological signals, such as ECG and Photoplethysmogram, in a fusion mode to identify and authenticate a person, resulting in enhancement of their privacy and security in a smart aging environment. The benefits offered by the fall detection system, early detection and identifying the signs of dementia, and the biometric authentication system, can improve the quality of life for the seniors who prefer to live independently or by themselves

A systematic review on machine learning models for online learning and examination systems

Examinations or assessments play a vital role in every student’s life; they determine their future and career paths. The COVID pandemic has left adverse impacts in all areas, including the academic field. The regularized classroom learning and face-to-face real-time examinations were not feasible to avoid widespread infection and ensure safety. During these desperate times, technological advancements stepped in to aid students in continuing their education without any academic breaks. Machine learning is a key to this digital transformation of schools or colleges from real-time to online mode. Online learning and examination during lockdown were made possible by Machine learning methods. In this article, a systematic review of the role of Machine learning in Lockdown Exam Management Systems was conducted by evaluating 135 studies over the last five years. The significance of Machine learning in the entire exam cycle from pre-exam preparation, conduction of examination, and evaluation were studied and discussed. The unsupervised or supervised Machine learning algorithms were identified and categorized in each process. The primary aspects of examinations, such as authentication, scheduling, proctoring, and cheat or fraud detection, are investigated in detail with Machine learning perspectives. The main attributes, such as prediction of at-risk students, adaptive learning, and monitoring of students, are integrated for more understanding of the role of machine learning in exam preparation, followed by its management of the post-examination process. Finally, this review concludes with issues and challenges that machine learning imposes on the examination system, and these issues are discussed with solutions