Unsupervised Gaze-aware Contrastive Learning with Subject-specific Condition

Appearance-based gaze estimation has shown great promise in many applications by using a single general-purpose camera as the input device. However, its success is highly depending on the availability of large-scale well-annotated gaze datasets, which are sparse and expensive to collect. To alleviate this challenge we propose ConGaze, a contrastive learning-based framework that leverages unlabeled facial images to learn generic gaze-aware representations across subjects in an unsupervised way. Specifically, we introduce the gaze-specific data augmentation to preserve the gaze-semantic features and maintain the gaze consistency, which are proven to be crucial for effective contrastive gaze representation learning. Moreover, we devise a novel subject-conditional projection module that encourages a share feature extractor to learn gaze-aware and generic representations. Our experiments on three public gaze estimation datasets show that ConGaze outperforms existing unsupervised learning solutions by 6.7% to 22.5%; and achieves 15.1% to 24.6% improvement over its supervised learning-based counterpart in cross-dataset evaluations.

FreeGaze: Resource-efficient Gaze Estimation via Frequency Domain Contrastive Learning

Gaze estimation is of great importance to many scientific fields and daily applications, ranging from fundamental research in cognitive psychology to attention-aware mobile systems. While recent advancements in deep learning have yielded remarkable successes in building highly accurate gaze estimation systems, the associated high computational cost and the reliance on large-scale labeled gaze data for supervised learning place challenges on the practical use of existing solutions. To move beyond these limitations, we present FreeGaze, a resource-efficient framework for unsupervised gaze representation learning. FreeGaze incorporates the frequency domain gaze estimation and the contrastive gaze representation learning in its design. The former significantly alleviates the computational burden in both system calibration and gaze estimation, and dramatically reduces the system latency; while the latter overcomes the data labeling hurdle of existing supervised learning-based counterparts, and ensures efficient gaze representation learning in the absence of gaze label. Our evaluation on two gaze estimation datasets shows that FreeGaze can achieve comparable gaze estimation accuracy with existing supervised learning-based approach, while enabling up to 6.81 and 1.67 times speedup in system calibration and gaze estimation, respectively.

Simultaneous Energy Harvesting and Gait Recognition using Piezoelectric Energy Harvester

Piezoelectric energy harvester, which generates electricity from stress or vibrations, is gaining increasing attention as a viable solution to extend battery life in wearables. Recent research further reveals that, besides generating energy, PEH can also serve as a passive sensor to detect human gait power-efficiently because its stress or vibration patterns are significantly influenced by the gait. However, as PEHs are not designed for precise measurement of motion, achievable gait recognition accuracy remains low with conventional classification algorithms. The accuracy deteriorates further when the generated electricity is stored simultaneously. To classify gait reliably while simultaneously storing generated energy, we make two distinct contributions. First, we propose a preprocessing algorithm to filter out the effect of energy storage on PEH electricity signal. Second, we propose a long short-term memory (LSTM) network-based classifier to accurately capture temporal information in gait-induced electricity generation. We prototype the proposed gait recognition architecture in the form factor of an insole and evaluate its gait recognition as well as energy harvesting performance with 20 subjects. Our results show that the proposed architecture detects human gait with 12% higher recall and harvests up to 127% more energy while consuming 38% less power compared to the state-of-the-art.