Facial expression identification over a time series of images - QC-202
Preferred Disciplines: Deep Learning, Artificial Intelligence, Computer Vision (Level: Master, PhD or Post-Doc)
Project length: 4-6 months (1 unit)
Approx. start date: As soon as possible
Location: Montreal, QC
No. of Positions: 1
We aim to disrupt the facial motion capture market by leveraging deep neural networks to map, deduce, and identify highly-realistic facial expressions. Our production software will replicate the work of the world’s best animators to yield superior quality facial animations with faster character turnout.
Summary of Project:
- Identify key facial expressions over a time series of images utilizing CNN and LSTM
- Interpolation between facial expressions utilizing AI.
- Head, eye, mouth, and eyelid tracking using computer vision.
- Sentiment analysis of facial expression (classification)
- Facial expression sentiment analysis and motion interpolation over a time series of images
- Identify facial expression over a time series of images
- Sentiment analysis of facial expression
- Interpolation between facial expression over time series
- Output data of facial expression interpolation and sentiment classification over time series.
Expertise and Skills Needed:
- Must: Deep Learning (with experience with CNN, RNN, LSTM)
- Preferable: Artificial Intelligence, Computer Vision
For more info or to apply to this applied research position, please