Adaptive facial point detection and emotion recognition for a humanoid robot

Zhang, Li, Mistry, Kamlesh, Jiang, Ming, Chin Neoh, Siew and Hossain, Alamgir (2015) Adaptive facial point detection and emotion recognition for a humanoid robot. Computer Vision and Image Understanding, 140. pp. 93-114. ISSN 1077-3142

Full text not available from this repository.
Official URL: http://dx.doi.org/10.1016/j.cviu.2015.07.007

Abstract

Automatic perception of facial expressions with scaling differences, pose variations and occlusions would greatly enhance natural human robot interaction. This research proposes unsupervised automatic facial point detection integrated with regression-based intensity estimation for facial action units (AUs) and emotion clustering to deal with such challenges. The proposed facial point detector is able to detect 54 facial points in images of faces with occlusions, pose variations and scaling differences using Gabor filtering, BRISK (Binary Robust Invariant Scalable Keypoints), an Iterative Closest Point (ICP) algorithm and fuzzy c-means (FCM) clustering. Especially, in order to effectively deal with images with occlusions, ICP is first applied to generate neutral landmarks for the occluded facial elements. Then FCM is used to further reason the shape of the occluded facial region by taking the prior knowledge of the non-occluded facial elements into account. Post landmark correlation processing is subsequently applied to derive the best fitting geometry for the occluded facial element to further adjust the neutral landmarks generated by ICP and reconstruct the occluded facial region. We then conduct AU intensity estimation respectively using support vector regression and neural networks for 18 selected AUs. FCM is also subsequently employed to recognize seven basic emotions as well as neutral expressions. It also shows great potential to deal with compound and newly arrived novel emotion class detection. The overall system is integrated with a humanoid robot and enables it to deal with challenging real-life facial emotion recognition tasks.

Item Type: Article
Uncontrolled Keywords: Facial point detection; Action unit; Pose variation; Occlusion; Emotion recognition; Human robot interaction
Subjects: G400 Computer Science
G700 Artificial Intelligence
Department: Faculties > Engineering and Environment > Computer Science and Digital Technologies
Depositing User: Paul Burns
Date Deposited: 29 Sep 2015 10:48
Last Modified: 03 Nov 2016 13:02
URI: http://nrl.northumbria.ac.uk/id/eprint/23899

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics


Policies: NRL Policies | NRL University Deposit Policy | NRL Deposit Licence