Machine Learning Blog

Monthly Archives: March 2017

Greg Slabaugh receives the Research Student Supervision Award 2017


Hosted and organised by the Students’ Union, the Learning Enhancement Awards allow City students to recognise staff for their support (

Member of the Machine Learning Research Centre, Greg Slabaugh received the Research Student Supervision award 2017, an award that covers all Schools at City, University of London.


Machine Learning seminar by Prof Duncan Gillies, Imperial College

News, Seminar.

Machine Learning seminar

Prof Duncan Gillies, Imperial College London

Monday, 20 March 2017, 2pm. College Building, room AG22

Title: Visual Cognition in Face Recognition

(work by C. E. Thomaz, V. Amaral, G. A. Giraldi, D. F. Gillies and D. Rueckert)

Abstract: Recently new methods have been emerging in the field of image recognition. In particular, deep neural networks have reached very high levels of accuracy out performing other learning paradigms on large image data bases However, although deep networks are biologically inspired they tell us very little about how human visual perception works. They can correctly classify a picture, but they cannot give us any interpretation of that picture. Human vision has been found to operate quite differently. When viewing a picture we tend to concentrate our gaze at a small number of key points rather than looking at it globally. Our efforts are directed towards interpreting rather than classifying. An interesting question is “is it possible to characterise and make use of the mechanisms of human visual perception?”

The talk will focus on face recognition, and introduce some recent work carried out as a joint project between Centro Universitario FEI and Imperial College on incorporating human visual cognition with existing face recognition algorithms. It will give a brief overview of the history of face recognition including Sirovich and Kirby’s work on face spaces using PCA and Yarbus’ eye tracking experiment on human perception. It will then go on to describe some experiments where human fixation points have been used to spatially weight the PCA face feature space leading to improved accuracy of recognition.

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