- B.Sc., M.Sc.Eastern Mediterranean University; Ph.D., University of Kent
- Computer vision, image analysis, image processing.
- Biometric systems, especially for iris, signature and handwriting biometrics.
- Ageing effects in biometric systems.
- Image segmentation, feature analysis and recognition algorithms for generic use, and particularly for applications in biometrics.
- CNG230 - Introduction to C programming
- CNG213 - Data Strctures
- CNG466 - Fundamental Image Processing Techniques
- M. Da Costa-Abreu, M. Fairhurst, M. Erbilek, “Age predictive biometrics: predicting age from iris characteristics”, Iris and Periocular Biometric Recognition, IET, Chap. 10, pp. 213-234, 2017.
- Y. Liang, M. Fairhurst, R.M. Guest and M. Erbilek, “Automatic handwriting feature extraction, analysis and visualisation in the context of digital palaeography”, International Journal of Pattern Recognition and Artificial Intelligence, vol.30, no.4, pp. 1653001, 2016.
- M. Fairhurst, M. Erbilek and C. Li, “A study of automatic prediction of emotion from handwriting samples to support forensic analysis”, IET Biometrics, vol.4, no.2, pp. 90-97, 2015.
- M. Fairhurst, M. Erbilek and M. C. D. C. Abreu, “Selective review and analysis of ageing effects in biometric system implementation”, IEEE Transactions on Human-Machine Systems, vol. 45, no.3, pp. 294-303, 2015.
- M. Erbilek and M. Fairhurst, “Analysis of ageing effects in biometric systems: difficulties and limitations”, Age factors in biometric processing, IET, pp. 279-301, 2013.
- M. Erbilek and M. Fairhurst, “A framework for managing ageing effects in signature biometrics”, IET Biometrics, vol.1, no.2, pp. 136-147, June 2012.
- M. Fairhurst and M. Erbilek, “Analysis of physical ageing effects in iris biometrics”, Future Trends in Biometric Processing, IET Computer Vision, vol.5, no.6, pp. 358-366, November 2011.