Modeling Eye Gaze Velocity Trajectories using GANs with Spectral Loss for Enhanced Fidelity

TL;DR

This paper presents a novel GAN framework with spectral loss for generating high-fidelity synthetic eye gaze velocity trajectories, outperforming traditional models in capturing complex temporal and spectral features.

Contribution

Introduces a spectral regularization-enhanced LSTM-CNN GAN architecture for more accurate eye gaze trajectory modeling, surpassing traditional HMMs and prior GANs.

Findings

LSTM-CNN GAN closely matches real data statistics

Spectral loss improves spectral fidelity and training stability

Outperforms HMM in modeling complex eye gaze dynamics

Abstract

Accurate modeling of eye gaze dynamics is essential for advancement in human-computer interaction, neurological diagnostics, and cognitive research. Traditional generative models like Markov models often fail to capture the complex temporal dependencies and distributional nuance inherent in eye gaze trajectories data. This study introduces a GAN framework employing LSTM and CNN generators and discriminators to generate high-fidelity synthetic eye gaze velocity trajectories. We conducted a comprehensive evaluation of four GAN architectures: CNN-CNN, LSTM-CNN, CNN-LSTM, and LSTM-LSTM trained under two conditions: using only adversarial loss and using a weighted combination of adversarial and spectral losses. Our findings reveal that the LSTM-CNN architecture trained with this new loss function exhibits the closest alignment to the real data distribution, effectively capturing both the distribution tails and the intricate temporal dependencies. The inclusion of spectral regularization significantly enhances the GANs ability to replicate the spectral characteristics of eye gaze movements, leading to a more stable learning process and improved data fidelity. Comparative analysis with an HMM optimized to four hidden states further highlights the advantages of the LSTM-CNN GAN. Statistical metrics show that the HMM-generated data significantly diverges from the real data in terms of mean, standard deviation, skewness, and kurtosis. In contrast, the LSTM-CNN model closely matches the real data across these statistics, affirming its capacity to model the complexity of eye gaze dynamics effectively. These results position the spectrally regularized LSTM-CNN GAN as a robust tool for generating synthetic eye gaze velocity data with high fidelity.