View Proposal

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Proposer

John See

Title

Spotting and Recognising Subtle Facial Expressions from RGB-D Data

Goal

An algorithm/model/technique for spotting and/or recognising facial micro-expressions, with clever utilisation of depth information from the data

Description

The human face is often the gateway to understanding a person's emotional state. More often than not, humans also possess the sheer ability to conceal their emotions when required but the leakage of such emotions also occurs in the form of subtle expressions (or "micro-expressions"). Research in computing micro-expressions has gained significant interest in the last 10 years due to the availability of public datasets captured from carefully elicited setups. Recently, two newly established large-scale multimodal databases: the CAS(ME)³ and 4DME have presented exciting opportunities for studying the computation of micro-expressions from a new perspective — one that aims to utilise additional depth information on top of the usual RGB data from videos. The aim of computing these micro-expressions is to find viable algorithms to locate the occurrence ("spotting") of the micro-expression in a video sequence, and thereafter, identify its emotion class ("recognition"). This is a research-centric project. The project scope could include designing algorithms for the 'spotting' task, or the 'recognition' task, or both.

Resources

GPU compute (access to MACS Malaysia server OR supervisor's GPU workstation)

Background

Good level of competency in programming, especially Python; Some familiarity with machine learning or deep learning techniques would be an added advantage.

Url

Difficulty Level

High

Ethical Approval

None

Number Of Students

1

Supervisor

John See

Keywords

subtle facial expressions, micro-expressions, deep learning, affective computing

Degrees

Bachelor of Science in Computing Science