Portrait of H Alsufyani

H Alsufyani

Research student

About

Hamed Alsufyani obtained his Bachelor degree in Computer Science from Taif University,Saudi Arabia. In 2014, he received his MSc degree in Information Security and Biometrics, from School of Engineering and Digital Arts, University of Kent, Canterbury, U.K where he is currently pursuing for the Ph.D. degree. His work focuses on exploring the potential of using biometrics and their application in person recognition. His research interests include computer vision, image processing, pattern recognition, and biometrics.

Research interests

Pattern Recognition, Information Fusion, Computer Vision and Image Processing

Teaching

2017 - 2018

  • Introduction to Programming
  • Microcomputer Engineering
  • Image Analysis with Security Applications
  • Biometric Technology
  • Advanced Pattern Recognition
  • Website Design
  • Signals and Systems
  • Internet Programming with Java

2016 - 2017

  • Introduction to Programming
  • Image Analysis with Security Applications
  • Biometric Technology
  • Advanced Pattern Recognition
  • Website Design
  • Internet Programming with Java

2015 - 2016

  • Introduction to Programming
  • Image Analysis with Security Applications
  • Biometric Technology

2014 - 2015

  • Image Analysis with Security Applications
  • Biometric Technology
  • Advanced Pattern Recognition

Publications

Conference or workshop item

  • Alsufyani, H., Hoque, S. and Deravi, F. (2017). Automated Skin Region Quality Assessment for Texture-based Biometrics. in: 2017 Seventh International Conference on Emerging Security Technologies (EST). IEEE, pp. 169-174. Available at: https://doi.org/10.1109/EST.2017.8090418.
    Designing a biometric system based solely on skin texture is of interest because the face is sometimes occluded by hair or artefacts in many real-world contexts. This work presents a novel framework for the assessment of skin-based biometric systems incorporating skin quality information. The quality or purity of the extracted skin region is automatically established using pixel colour models prior to biometric processing. Facial landmarks are detected to facilitate automated extraction of facial regions of interest. Although the present study is confined to the forehead region, the idea can be extended to other skin regions. Local Binary Patterns (LBP) and Gabor wavelet filters are utilised to extract skin features. Using the publicly available XM2VTS database, the experimental results show that the system provides promising performance when compared to other commonly used techniques.
  • Alsufyani, H., Hoque, S. and Deravi, F. (2016). Exploring the Potential of Facial Skin Regions for the Provision of Identity Information. in: The 7th IET International Conference on Imaging for Crime Detection and Prevention (ICDP-16).. Available at: http://dx.doi.org/10.1049/ic.2016.0084.
    This work presents a novel framework to investigate the possibility of using texture information from facial skin regions for biometric person recognition. Such information will be practically useful when the entire facial image is not available for identifying the individuals. Four facial regions have been investigated (i.e. forehead, right cheek, left cheek, and chin) since they are relatively easy to distinguish in frontal images. Facial landmarks are automatically detected to facilitate the extraction of these facial regions of interest. A new skin detection technique is applied to identify regions with significant skin content. Each such skin regions are then processed independently using features based on Local Binary Patterns and Gabor wavelet filters. Feature fusion is then used prior to classification of the images. Experiments were carried out using the publicly available Skin Segmentation database and the XM2VTS databases to evaluate the skin detection technique and the biometric recognition performances respectively. The results indicate that the skin detection algorithm provided an acceptable results when compared with other state-of-the-art skin detection algorithms. In addition, the forehead and the chin regions where found to provide a rich source of biometric information.