Science and Engineering

Vision and signal processing



Our research focuses on computer vision, machine learning, pattern recognition and signal processing. We have applications in diverse areas, including:

  • video surveillance
  • biometrics
  • speech processing
  • aerial image processing
  • wireless communications
  • human-computer interaction
  • video search and retrieval
  • cognitive radio
  • electromagnetics
  • medical imaging.

The algorithms and computational principles of vision, which are a key part of our research, also underpin many fields of autonomous vision, including computer vision and robotic vision.

Vision is one of the most important contributors to automation, and we're leaders in building systems that automatically interpret visual scenes.

Our systems can infer, extract meaning, identify underlying structures, and recognise people and their activities across a large variety of environments.

Featured research

Our researchers collaborate on projects in specialised research groups and facilities across disciplines and institutions:


The Category 1 funded research projects we are currently leading are:

Solve it or ignore it? The challenge of alignment distortion and creating next generation automatic facial expression detection

Project leader
Emeritus Professor Sridha Sridharan
Project summary

The last two decades have seen an escalating interest in automating the coding of facial expressions. Despite this keen interest, the promise of computer vision systems to accurately code facial expressions in natural circumstances remains elusive.

Our interdisciplinary team will research a new paradigm to account for facial alignment distortion directly rather than aiming to achieve invariance to it. The project will also research new data agnostic feature compaction capabilities to enable scalable learning on the world's largest and challenging expression dataset available to us through international collaboration. Tackling these two major open problems will make accurate coding of facial expressions in natural environments achievable.

The next generation speaker recognition system

Project leader
Emeritus Professor Sridha Sridharan
Project summary
The next generation of speaker recognition technologies developed through this project will enable secure person authentication by voice in financial transactions and benefit the community through the elimination of identity fraud. This project will safeguard Australia by identifying criminal suspects using their voice and combat terrorism by using voice to locate and track terrorists.

Interdisciplinary projects

Our interdisciplinary Category 1 funded research projects are:

Improving productivity and efficiency of Australian airports – a real time analytics and statistical approach

Discipline lead
Associate Professor Clinton Fookes
Project summary

Aviation is a major economic driver both within Australia and overseas, but the aviation industry faces growing challenges from the increase in passengers and changing regulations. To meet these challenges, airports, airlines, government agencies and others need to maximise their efficiency and productivity. However, complex dependencies and differing operational objectives complicate this task.

This project aims to develop a real-time, whole-of-system operational performance framework that can help operators in finding and evaluating solutions to maximise throughput, reduce wait times and mitigate flow-on effects. Innovative new video analytic and Bayesian Network based tools are integrated to address the challenges of adaptability and uncertainty.

Monitoring intuitive expertise in the context of airport security screening

Discipline lead
Associate Professor Clinton Fookes
Project summary

During airport security screening and processing, confusion and error are greatest when systems or contexts are unfamiliar. Poorly designed systems compromise the interactions of airport security personnel and decrease their ability to promptly and accurately respond to situations.

This project aims to deliver a suite of automated methods to monitor security operator knowledge and engagement, to assess the real-time security screening context, and to detect unusual passenger behavior at the screening check-point. This monitoring aims to provide new knowledge and techniques to enhance security operator performance, refine the screening process, improve passenger experience and, most critically, ensure safety at Australian airports.

Student topics

Are you looking to further your career by pursuing study at a higher and more detailed level? We are currently looking for students to research with us. Contact our staff to find out more about research opportunities, or take a look at our student topics.


School of Electrical Engineering and Computer Science

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    Gardens Point
  • Postal address:
    School of Electrical Engineering and Computer Science
    GPO Box 2434
    Brisbane QLD 4001