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Lecturer of General Engineering (Information Communication System)

DK Dr Hayati Pg Hj Mohd Yassin
Lecturer of General Engineering (Information Communication System)

Office

Staff Room 5, C1-8, First Floor, Block C, Integrated Sciences Building

Email

hayati.yassin@ubd.edu.bn

Education/Professional Qualifications

  • PhD in Electrical Engineering, University of Kent, Canterbury UK
  • MSc in Information Security and Biometrics, University of Kent, Canterbury UK
  • BSc in Computer Network Security, Birmingham City University, UK

Teaching Areas

Fundamentals Programming for Engineer, Digital Communication System, Engineering Research Methodologies, Mobile and Wireless Network Systems, Information Communication Security

Current Research Activities

  • Wave and tide prediction for Brunei deep-water operation (Collaboration project)
  • AIMS for Solar PV projects in Brunei Darussalam (Collaboration with Ministry of Energy)
  • Biometrics: Face ageing performance in face recognition
  • Artificial Neural Network
  • Pattern recognition on biodiversity

Recent Publications

Future Projects

Research Profile (UBD expert): https://expert.ubd.edu.bn/hayati.yassin

IMPACT OF AGE AND AGEING ON FACE RECOGNITION PERFORMANCE The effect of ageing on biometric systems and particularly its impact on face recognition systems is a challenging area for research. Face ageing in humans is the result of multi-dimensional process of physical, physiological, and social change, which affects considerably the appearance of a human face. Being biological tissue in nature, facial biometric trait undergoes significant changes as a person ages. Ageing-related appearance variation due to bone growth normally occurs throughout childhood and puberty, whereas skin-related effects principally appear in older subjects. Looking from the face recognition point of view, ageing of the face images of the same person brings confusion which degrades the system performance dramatically. In many practical systems (e.g., passport control, etc.), the time intervals between two acquired images can lead up to several years. The ageing factor is very significant in face images. In comparison to other facial variation (pose, illumination, etc.), adaption to template ageing deserves a dedicated treatment of its own, since ageing is a lifelong process. Ageing also bring gradual changes in the data distribution over time, thus causing performance loss as a result of template becoming outdated. These factors indicate that template ageing process is very similar to the concept drift theory, based on the fact that real-worlds concepts change with the time resulting in underlying data distribution to change. When compared with other source of variation in face images, ageing variations is specific to a given individual. It can occur slowly and is affected significantly by other factors. The appearance of a human face is affected considerably by the ageing process.

Research Interests

  • Image processing, pattern recognition and computer vision
  • Data analytics, artificial intelligence, machine learning and deep learning techniques
  • Biometrics modalities
  • Renewable energy
  • Cyber security technologies