JCSE, vol. 17, no. 2, pp.41-50, 2023

DOI: http://dx.doi.org/10.5626/JCSE.2023.17.2.41

Looking to Personalize Gaze Estimation Using Transformers

Seung Hoon Choi, Donghyun Son, Yunjong Ha, Yonggyu Kim, Seonghun Hong, and Taejung Park
VisualCamp, Seoul, South Korea Department of Cybersecurity, Duksung Women's University, Seoul, South Korea

Abstract: Anatomical differences between people restrain the accuracy of appearance-based gaze estimation. These differences can be taken into account with few-shot approaches for further optimization. However, these approaches come with additional computational complexity cost and are vulnerable to corrupt data inputs. Consequently, the use of accurate gaze estimation in real-world scenarios is restricted. To solve this problem, we introduce a novel and robust gaze estimation calibration framework called personal transformer-based gaze estimation (PTGE), utilizing a deep learning network that is separate from the gaze estimation model to adapt to new users. This network learns to model and estimate person-specific differences in gaze estimation as a low-dimensional latent vector from image features, head pose information, and gaze point labels. The expensive computational optimization process in few-shot approaches is removed in PTGE through our separate network. This separate network is composed of transformers, allowing self-attention to weigh the quality of calibration samples and mitigate the negative effects of corrupt inputs. PTGE achieves near state-of-the-art performance of 1.49 cm on GazeCapture with a small number of calibration samples (<=16) and no optimization when adapting to a new user, only a 2% decrease from the state-of-the-art achieved without the hour-long optimization process.

Keyword: Gaze estimation; Transformer; Artificial intelligence; Human computer interaction; Personal calibration

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