Human Sensing

Enabling computers to understand and characterize human behaviour has the potential to revolutionize many areas that benefit society such as clinical diagnosis, human computer interaction, and social robotics. In the Human Sensing (HS) laboratory we are interested in modelling and characterizing human behaviour from a variety of sensory data (e.g., video, motion capture, wearable sensors). Current projects in the HS laboratory include:

  • 1- Activity and event detection from video
  • 2- Depression detection from multimodal data (e.g., RGBD video, binary sensors and accelerometers)
  • 3- Monitoring Parkinson's desease with wearable sensors
  • 4- Automatic stress analysis from psychological measurements (e.g., EEG, GSR, heart rate)
  • 5- Hot-flash detection with wearable GSR sensors
  • 6- Personalization of Machine Learning algorithms
For more information about the HS laboratory see Human Sensing

Component Analysis

Component Analysis (CA) methods (e.g. kernel PCA, Support Vector Machines, Spectral Clustering) are a set of algebraic techniques that decompose a signal into components that are relevant for a given task (e.g., classification, clustering). In the CA laboratory we are interested in extending CA techniques for classification, clustering, modelling and visualization of large amounts of high-dimensional data. Current projects in the CA laboratory include:

  • 1- Robust and regularized subspace learning methods
  • 2- Temporal clustering and alignment
  • 3- Weakly supervised learning
  • 4- Time series (supervised, unsupervised, weakly-supervised)
  • 5- Graph Matching (i.e., quadratic assignment problems)
  • 6- Kernel Methods
For more information on the CA laboratory see Component Analysis

Face Analysis

I am interested in facial image analysis. Several of our current research projects explore the use of facial behaviour as a predictor of internal states, social behaviour, biometrics and psychopathology. Current projects on facial image analysis include:

  • 1- Facial feature detection and tracking
  • 2- Facial Expression Analysis
  • 3- Face Recognition
  • 4- Facial Expression Transfer
  • 5- Face De-identification
  • 6- Facial attribute estimation (e.g., age, ethnicity)
  • 7- Facial attribute removal (e.g., beard or glasses removal)
See our software for automatic facial image analysis.