Towards High Performance Low Complexity Calibration in Appearance Based Gaze Estimation.

Appearance-based gaze estimation from RGB images provides relatively unconstrained gaze tracking from commonly available hardware. The accuracy of subject-independent models is limited partly by small intra-subject and large inter-subject variations in appearance, and partly by a latent subject-dependent bias. To improve estimation accuracy, we have previously proposed a gaze decomposition method that decomposes the gaze angle into the sum of a subject-independent gaze estimate from the image and a subject-dependent bias. Estimating the bias from images outperforms previously proposed calibration algorithms, unless the amount of calibration data is prohibitively large. This paper extends that work with a more complete characterization of the interplay between the complexity of the calibration dataset and estimation accuracy. In particular, we analyze the effect of the number of gaze targets, the number of images used per gaze target and the number of head positions in calibration data using a new NISLGaze dataset, which is well suited for analyzing these effects as it includes more diversity in head positions and orientations for each subject than other datasets. A better understanding of these factors enables low complexity high performance calibration. Our results indicate that using only a single gaze target and single head position is sufficient to achieve high quality calibration. However, it is useful to include variability in head orientation as the subject is gazing at the target. Our proposed estimator based on these studies (GEDDNet) outperforms state-of-the-art methods by more than 6.3%. One of the surprising findings of our work is that the same estimator yields the best performance both with and without calibration. This is convenient, as the estimator works well "straight out of the box," but can be improved if needed by calibration. However, this seems to violate the conventional wisdom that train and test conditions must be matched. To better understand the reasons, we provide a new theoretical analysis that specifies the conditions under which this can be expected. The dataset is available at http://nislgaze.ust.hk. Source code is available at https://github.com/HKUST-NISL/GEDDnet.

A novel benzotriazole derivate with Twisted intramolecular charge transfer and Aggregation Induced emission features for proton determination.

Molecules with Aggregation-Induced Emission (AIE) effects could show strong emission in solid or aggregate form, thus they are suitable for applications in the field of solid luminescent materials. According to former reporting, AIE molecules are always J-aggregates. In this study, a new benzotriazole derivate with electron donor-acceptor structure was synthesized. (E)-4-(2-(1H-benzo[d] [1,2,3] triazol-1-yl) vinyl)-N, N-dimethylaniline (BTADA) has both TICT and AIE effect, even though it is H-aggregate. Furthermore, BTADA could respond to proton in solution and solid form. Due to its multiple binging sites, proton would bind to benzotriazole and dimethylaniline moiety successively when BTADA was exposed to acid, and the fluorescent color changed from green to yellow then blue as the concentration of proton increased. Such phenomena indicate BTADA has potential usage in proton detection.