Research Experience
Postdoctoral Research Associate, University of Technology, Sydney (UTS), Faculty of Information Technology, Computer Vision Research Group (CVRG) (Dec. 2006 - Sep. 2008)
I worked as a postdoctoral research associate on the Australian Research Council (ARC) funded Linkage Project titled "Automatic real-time detection of infiltrated objects for security of airports and train stations". This project has developed computer vision-based technologies capable of detecting/recognizing infiltrated objects in sensitive areas (e.g., train station) and track people through repeated short-term occlusions. This research has been conducted under the supervision of Prof. Massimo Piccardi.
Ph.D. Research, University of Technology, Sydney (UTS), Faculty of Information Technology, Computer Vision Research Group (CVRG) (2003-2006)
My PhD research focused on building a multi-modal/cue module that can extract features from expressive face and upper-body gestures (e.g., shoulder movements and hand gestures) using computer vision and image processing techniques and integrate these features using machine learning and pattern recognition techniques. During my PhD research I explored novel research grounds such as multi-modal/cue affective database creation from multiple sensors and annotation of such data (differences in interpretation and labelling: face vs. face-and-body), detection of temporal phases (neutral, onset, apex and offset) decoupling temporal phases from the spatial features in order to achieve higher accuracy in affective state recognition, synchronization of the bimodal data to the purpose of multimodal fusion, and obtaining optimal fusion by experimenting with various strategies. At the time of its completion, it was one of the first attempts reported in the literature to recognise human affect like happiness and sadness from multiple visual cues such as face and body gestures. Another major contribution of my PhD work was the FABO database containing labelled videos of posed face-and-body affective displays. This database has been made publicly available for research purposes. This research was conducted under the supervision of Prof. Massimo Piccardi.
Visiting Ph.D. Student, Delft University of Technology, Faculty of Electrical Engineering, Mathematics and Computer Sciences, Man-Machine Interaction Group, Delft, The Netherlands (Nov. 2005- Feb. 2006)
The focus of my visit was on finding how humans use their face and body when they are communicating affectively in natural settings and how their face and body displays differ when they are posing upon request for affective data acquisition. Thus, the first aim of this research was to obtain visual spatiotemporal analysis of posed versus spontaneous facial and bodily expression by exploring data acquired both in laboratory and (more) natural settings. The next step in this research was to automatically detect, recognize and compare these cues in a multi- cue/modal manner and conclude whether the observed differences were sufficient for a machine to learn to distinguish between spontaneous vs. posed affective displays. Overall, it was possible to distinguish between posed and spontaneous smiles by fusing information coming from head, face, and shoulder channels at different levels of abstraction (i.e., early, mid-level, and late fusion). The results obtained contributed to the field of Automatic Analysis of Human Spontaneous Behaviour that aims to develop natural human machine interfaces by analysing the affective state of the users. The research was conducted under the supervision of A/Prof. Maja Pantic.
Research Assistant, University of Technology, Sydney (UTS), Faculty of Information Technology, Computer Vision Research Group (CVRG) (Feb. 2003- Feb. 2004)
My research during this period tackled the problem of automatic assessment of aesthetics. In the first part of this research, we evaluated the extent of universality of aesthetics by asking a diversified set of human referees to grade a collection of female facial images in terms of their facial aesthetics. Results obtained showed that the different individuals generally provided unimodal and compact grade histograms, thus well supporting the concept that perception of aesthetics is universal to a certain degree. Later, we introduced an approach to automatically measure aesthetics based on automated extraction of facial features and supervised classification. We presented an efficient procedure for automatically measuring facial features from face images by means of image analysis operators. For supervised classification, we used such extracted facial features and the average human grades from a set of images to train an automated classifier. The accuracy achieved on an independent test set and from cross-validation proved that the classifier can be effectively used as an automated tool to reproduce an ��average�� human judgement on facial aesthetics.