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Norman Poh

Norman Poh

Department of Computer Science
Faculty of Engineering and Physical Sciences
University of Surrey, UK

Talk I: Biometrics performance evaluation

One of the major sources of variability in assessing the performance of a biometric system is the subject variability. Testing the same system on two disjoint populations of users almost always invariably yields two different results. The talks will enable participants to understand how the error rates such as false match and false nonmatch rates are derived, as well as their confidence intervals. A recent technique that can extrapolate the system performance across various factors will also be presented, along with the source codes made vailable for participants to try.

Talk II: Countering facial anti spoofing

Rendering a face recognition system robust is vital in order to safeguard it against spoof attacks carried out by using printed pictures of a victim (also known as print attack) or a replayed video of the person (replay attack). A key property in distinguishing a live, valid access from printed media or replayed videos is by exploiting the information dynamics of the video content, such as blinking eyes, moving lips, and facial dynamics. I will first present the state of the art in facial anti-spoofing covering a range of techniques. 

In the second part, I will talk about a recently developed algorithm called Dynamic Mode Decomposition (DMD) as a general-purpose, entirely data-driven approach to capture the above liveness cues. The proposed classification pipeline consists of DMD, Local Binary Patterns (LBP), and Support Vector Machines (SVM) with a histogram intersection kernel. A unique property of DMD is its ability to conveniently represent the temporal information of the entire video as a single image with the same dimensions as those images contained in the video. The pipeline of DMD+LBP+SVM proves to be efficient, convenient to use, and effective. In fact only the spatial configuration for LBP needs to be tuned. The effectiveness of the methodology was demonstrated using three publicly available databases: print-attack, replay-attack, and CASIA-FASD, attaining comparable results with the state of the art, following the respective published experimental protocols.

Participants will be able to try out the algorithms implemented using Matlab.


Norman Poh is a Lecturer in Computational Intelligence, Department of Computer Science, University of Surrey.  He received the Ph.D. degree in computer science in 2006 from the Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland. Prior to this appointment, he was a Research Fellow with the Centre for Vision, Speech, and Signal Processing (CVSSP), University of Surrey, Guildford, Surrey, UK and a research assistant at IDIAP research institute, Swtizerland. His research interests focus on developing and applying pattern recognition theories to biometrics, information fusion, and healthcare informatics. In these areas, he has authored more than 70 peer-reviewed publications.

He recieved two personal Fellowships from the Swiss National Science Foundation (Young Prospective and Advanced Researcher grants) and authored five best paper awards (AVBPA’05, ICB’09, HSI 2010, ICPR 2010 and Pattern Recognition Journal 2006). He won the Researcher of the Year 2011 Award, University of Surrey. His project Exo-brain won several prizes in the ICC2013.

He is an Associate Editor of the IET Biometrics Journal, an IEEE Certified Biometrics Professional and trainer, a member of IEEE and IAPR, and a member of the Education Committee of the IEEE Biometric Council. He is the director of Biometrics School (Winter/Asia editions): www.biometricschool.org