Automatic Affective Behaviour Monitoring through audio-visual information while preserving user’s privacy

   College of Medicine and Veterinary Medicine

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  Prof Saturnino Luz, Prof Sotirios Tsaftaris, Dr Fasih Haider  No more applications being accepted  Funded PhD Project (UK Students Only)

About the Project

Please ensure you check the eligibility criteria before applying to this project.

The Advanced Care Research Centre (ACRC) is a new, multi-disciplinary, £20M research centre at the University of Edinburgh. The ACRC will lead society’s response to the grand challenge of an ageing population that is growing in size, longevity and needs through the pursuit of research intended to deliver “high‐quality data‐driven, personalised and affordable care to support the independence, dignity and quality‐of‐life of people living in their own homes and in supported care environments”.

This project sits within the ACRC Academy , a dedicated Centre for Doctoral Training, co-located with the ACRC, whose students will deliver key aspects of the ACRC research agenda through a new doctoral-level research and training programme that will also equip them for careers across a wide range of pioneering and influential leadership roles in the public, private and third sectors.

The PhD with Integrated Study in Advanced Care is a novel, structured, thematic, cohort-based, programme of 48 months duration. Each PhD research project within the Academy has been devised by a supervisory team comprising academic staff from at least two of the three colleges within the University of Edinburgh. Each annual cohort of around twelve will include students with disciplinary backgrounds spanning from engineering and data science to humanities, social science, business and commerce, social work, medicine and related health and care professions. This unique level of diversity is a key attribute of our programme.



To research and develop technology for monitoring mental health status over time through audio-visual information while addressing privacy issues for collection of audio and video data in the patient’s or care giver’s environment. 


  • To develop audio-visual data collection methods and tools 
  • To assess the privacy requirements of users 
  • Development of privacy-preserving automatic affect recognition methods 
  • To evaluate these methods with respect to performance and privacy preservation.  


Automatic affect recognition technologies can monitor a person’s mood and mental health by processing verbal and non-verbal cues extracted from the person’s audio-video data. However, the audio-video signal contains biometric and other personal information which can, if improperly handled, threaten the speaker’s privacy. Hence there is a need for automatic inference and monitoring methods that preserve privacy for audio-video data in terms of collection, training of machine learning models and use of such models in prediction. This project will focus on research, implementation and assessment of solutions for handling of audio-video data in the user’s own environment while protecting their privacy.  


Candidates will typically have an undergraduate degree in computer science, electrical engineering, physics, mathematics, or related subjects. Knowledge of and/or interest in signal processing, privacy-based machine learning, mobile application development and affective computing would be desirable.  

We are specifically looking for applicants who will view their cutting-edge PhD research project in the context of the overall vision of the ACRC, who are keen to contribute to tackling a societal grand challenge and who can add unique value to – and derive great benefit from – training in a cohort comprising colleagues with a very diverse range of disciplines and backgrounds. We advise prospective candidates to engage in dialogue with the named project supervisor and/or the Director of the Academy prior to submitting an application. 

You must read How to apply prior to application

Please Apply here

Computer Science (8) Engineering (12) Mathematics (25) Physics (29)

Funding Notes

PhDs are funded with an enhanced stipend for the full 4 year period.
The call is open to candidates of any nationality but funded places for overseas nationals will be strictly limited to 4 international students who can apply for the highly competitive ACRC Global Scholarship.
It is essential to read the How to Apply section of our website before you apply:
Please apply here:


Haider, F. and Luz, S., 2019. A System for Real-Time Privacy Preserving Data Collection for Ambient Assisted Living. In INTERSPEECH (pp. 2374-2375).
Haider, F., Pollak, S., Albert, P. and Luz, S., 2020. Emotion recognition in low-resource settings: An evaluation of automatic feature selection methods. Computer Speech & Language
Haider, F. and Luz, S., 2019, May. Attitude recognition using multi-resolution cochleagram features. In 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 3737-3741). IEEE.
Haider, F., Salim, F.A., Conlan, O. and Luz, S., 2018. An Active Feature Transformation Method for Attitude Recognition of Video Bloggers. In INTERSPEECH (pp. 431-435).
Haider, F., Cerrato, L.S., Luz, S. and Campbell, N., 2016, November. Attitude recognition of video bloggers using audio-visual descriptors. In Proceedings of the Workshop on Multimodal Analyses enabling Artificial Agents in Human-Machine Interaction (pp. 38-42).
Haider, F., Albert, P. and Luz, S., 2021. User identity protection in automatic emotion recognition through disguised speech. AI, 2(4), pp.636-649.
Nautsch, A., Jiménez, A., Treiber, A., Kolberg, J., Jasserand, C., Kindt, E., Delgado, H., Todisco, M., Hmani, M.A., Mtibaa, A. and Abdelraheem, M.A., 2019. Preserving privacy in speaker and speech characterisation. Computer Speech & Language, 58, pp.441-480.

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