Biometric recognition is a rapidly evolving field that uses physical and behavioral characteristics to accurately identify individuals. Some common biological characteristics include fingerprints, facial features, palm prints, iris patterns, and palm veins, among others. Behavioral characteristics, such as the way one walks, talks, and types can also be used for identification purposes. Biometric recognition systems typically use a combination of biological and behavioral characteristics to achieve greater accuracy and robustness. Such systems have a wide range of applications in areas such as law enforcement, border control, financial institutions, healthcare, and access control systems.

Biometric recognition can be used for both authentication and identification purposes. Authentication is the process of verifying the identity of a user based on their biometric data and is critical for ensuring that the right person is granted access to the right information or resources. On the other hand, identification involves matching biometric data against a database of known identities to determine who the individual is.

There are three categories of biometric authentication: "Something You Own," "Something You Memorize," and "Something You Carry." Biometrics falls under the "Something You Own" category since it is part of the individual's physical identity.

While biometric recognition offers significant advantages in terms of security and convenience, there are also concerns about privacy and security. Biometric data is sensitive, and its theft or misuse can have severe consequences. In addition, the proliferation of biometric recognition systems has raised concerns among civil rights advocates about the potential for abuse and privacy violations.

As a result, there is a need to balance privacy and security concerns in biometric systems. Future directions for biometric recognition include improving accuracy and reducing error rates, developing new techniques based on deep neural networks for capturing and analyzing biometric data, and addressing privacy and security concerns through the development of robust data protection and encryption protocols.

Speaker

A/Prof. Waleed H. Abdulla

Waleed H. Abdulla holds a Ph.D. from the University of Otago, New Zealand, and has been working at the University of Auckland since 2002, where he currently holds the position of Associate Professor. He has made numerous contributions to the Asia Pacific ignaand Information Processing Association (APSIPA), including being part of the steering committee that initiated APSIPA in 2009. Additionally, he has served as APSIPA Vice President for Member Relations and Development for two terms, founded and served as the first Editor-in-Chief of the APSIPA Newsletter, and was awarded the distinction of APSIPA Distinguished Lecturer. He currently serves as a member of the APSIPA Board of Governors.

Waleed has presented three half-day tutorials on behalf of himself and collaborators at APSIPA conferences, including Active Noise Control in 2013, Speaker Recognition in 2014, and Human Biometrics: Fundamentals and Deep Learning Approach in 2023. The audience received them very well. He has worked as a Visiting Researcher/Collaborator with several international institutions, including Tsinghua University, Siena University (Italy), Essex University (UK), IDIAP (Switzerland), TIT (Japan), ETRI (Korea), HKPU (Hong Kong), and the Czech Republic. He has published over 200 refereed publications, including a patent and a book, with more than 2800 citations. He serves on the editorial boards of six journals and has supervised more than 30 PhD students. He has received numerous awards and funded projects, including fellowships from JSPS, ETRI, and Tsinghua, and has received the award for Excellent Teaching twice (in 2005 and 2012) as well as the Otago University Scholarship. He is a Life-Member of APSIPA and a Senior Member of IEEE.

Waleed's expertise lies in signal processing, analysis, and recognition. He is primarily interested in developing algorithms for various applications, including Human Biometrics, Speech Processing, Speech and Speaker Recognition, Hyperspectral Imaging, Active Noise Control, Speech Enhancement, Audio Watermarking, and EEG signal analysis.