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Hybrid description methods for human faces

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

About This PhD Project

Project Description

The group has pioneered the application of conditional probability density estimation to describing human faces. Our methods allow a system to estimate the age, sex, race and facial expression of people in a real-time video sequence. Accuracy is acceptable for some uses but not for demanding security applications where lighting may be poor and faces partly occluded. To tackle these difficult cases, we will combine our methods with new approaches in face landmark extraction, motion analysis and cascade classification. This project will explore these broad alternatives before focusing on promising avenues that will result in high-accuracy, high-speed robust face description.

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