Over the last decade, facial landmark tracking and 3D reconstruction have gained considerable attention due to their numerous applications such as human-computer interactions, facial expression analysis, and emotion recognition, etc. Traditional approaches require users to be confined to a particular location and face a camera under constrained recording conditions (e.g., without occlusions and under good lighting conditions). This highly restricted setting prevents them from being deployed in many application scenarios involving human motions. In this paper, we propose the first single-earpiece lightweight biosensing system, BioFace-3D, that can unobtrusively, continuously, and reliably sense the entire facial movements, track 2D facial landmarks, and further render 3D facial animations. Our single-earpiece biosensing system takes advantage of the cross-modal transfer learning model to transfer the knowledge embodied in a high-grade visual facial landmark detection model to the low-grade biosignal domain. After training, our BioFace-3D can directly perform continuous 3D facial reconstruction from the biosignals, without any visual input. Without requiring a camera positioned in front of the user, this paradigm shift from visual sensing to biosensing would introduce new opportunities in many emerging mobile and IoT applications. Extensive experiments involving 16 participants under various settings demonstrate that BioFace-3D can accurately track 53 major facial landmarks with only 1.85 mm average error and 3.38\% normalized mean error, which is comparable with most state-of-the-art camera-based solutions. The rendered 3D facial animations, which are in consistency with the real human facial movements, also validate the system's capability in continuous 3D facial reconstruction.
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Towards the Synthesis of Parent-Infant Facial Interactions
This work is motivated by the need to automate the analysis of parent-infant interactions to better understand the existence of any potential behavioral patterns useful for the early diagnosis of autism spectrum disorder (ASD). It presents an approach for synthesizing the facial expression exchanges that occur during parent-infant interactions. This is accomplished by developing a novel approach that uses landmarks when synthesizing changing facial expressions. The proposed model consists of two components: (i) The first is a landmark converter that receives a set of facial landmarks and the target emotion as input and outputs a set of new landmarks transformed to match the emotion. (ii) The second component involves an image converter that takes in an input image, a target landmark and a target emotion and outputs a face transformed to match the input emotion. The inclusion of landmarks in the generation process proves useful in the generation of baby facial expressions; babies have somewhat different facial musculature and facial dynamics than adults. This paper presents a realistic-looking matrix of changing facial expressions sampled from a 2-D emotion continuum (valence and arousal) and displays successfully transferred facial expressions from real-life mother-infant dyads to novel ones.
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- Award ID(s):
- 1846076
- NSF-PAR ID:
- 10321197
- Date Published:
- Journal Name:
- 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/FG52635.2021.9667019
