Upcoming Webinar, 2 July 2021: Facial Expression Analysis with Attention Mechanism

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Upcoming Webinar, 2 July 2021: Facial Expression Analysis with Attention Mechanism

Upcoming SPS Webinar

Title: Facial Expression Analysis with Attention Mechanism
Date: 2 July 2021
Time: 9:00 AM ET (New York time)
Duration: Approximately 1 Hour
Presenters: Dr. Jiabei Zeng

Based on the IEEE Xplore® article: Occlusion Aware Facial Expression Recognition Using CNN With Attention Mechanism
Published: IEEE Transactions on Image Processing, December 2018
Download: Original article will be made freely available for download for 48 hours from the day of the webinar, on IEEE Xplore®

 

Register for the Webinar

 

The IEEE Signal Processing Society would like to express our concern and support for the members of our global community and all affected by the current COVID-19 pandemic. We appreciate your continued patience and support as we work together to navigate these unforeseen and uncertain circumstances. We hope that you, your families, and your communities are safe!

About this topic:

Facial expressions are configurations of different muscle movements in the face. The local characters of muscle movements play an important role in distinguishing facial expressions by machines. In this webinar, the presenter will explore the local characters local characters of muscle movements by introducing the attention mechanism into two frameworks: 1) Propose a supervised convolutional neural network (CNN) with attention mechanism to recognize the facial expression with partially occluded faces. Through the attention module, the weights for different facial regions were understood along with the perceived occluded regions of the face and focused on the most discriminative unoccluded regions. 2) Propose a self-supervised facial action representation learning framework, where the attention mechanism is embedded in the encoder. Through the attention module, the result was to be able to discover the discriminative facial regions in an unsupervised manner and the ability to achieve self-supervised representation that improves the performance of facial action unit detection.


About the presenter:

Jiabei Zeng

Dr. Jiabei Zeng received the B.S. degree in computer science from Beihang University, Beijing, China, in 2011 and the Ph.D. degree in computer science from Beihang University, Beijing, China, in 2017.

From 2013 to 2015, she was a visiting scholar with the Robotics Institute, Carnegie-Mellon University, PA, USA. From 2017 to present, she has been an assistant professor with Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China. Dr. Zeng’s research interest includes computer vision and affective computing, especially developing algorithms to analyze human’s facial expression, facial actions, and gaze.

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