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2017 GTC San Jose

S7176 - Dynamic Facial Analysis: From Bayesian Filtering to Recurrent Neural Networks

Session Speakers
Session Description

We propose to use recurrent neural networks for analyzing facial properties from videos. Facial analysis from consecutive video frames, including head pose estimation and facial landmark localization, is key for many applications such as in-car driver monitoring, facial animation capture, and human-computer interaction. Compared with the traditional Bayesian filtering methods for facial tracking, we show RNNs are a more generic, end-to-end approach for joint estimation and tracking. With the proposed RNN method, we achieved state-of-the-art performance for head pose estimation and facial landmark localization on benchmark datasets.


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Talk
AI for In-Vehicle Applications Computer Vision and Machine Vision Deep Learning and AI Media and Entertainment
Automotive Defense Games Higher Education / Research Media & Entertainment
25 minutes
Session Schedule