Eugene Matthew. Taranta
University of Central Florida, Orlando, Florida, United States
Gesture recognition systems using nearest neighbor pattern matching are able to distinguish gesture from non-gesture actions by rejecting input whose recognition scores are poor. However, in the context of gesture customization, where training data is sparse, learning a tight rejection threshold that maximizes accuracy in the presence of continuous high activity (HA) data is a challenging problem. To this end, we present the Voight-Kampff Machine (VKM), a novel approach for rejection threshold selection. VKM uses new synthetic data techniques to select an initial threshold that the system thereafter adjusts based on the training set size and expected gesture production variability. We pair VKM with a state-of-the-art custom gesture segmenter and recognizer to evaluate our system across several HA datasets, where gestures are interleaved with non-gesture actions. Compared to alternative rejection threshold selection techniques, we show that our approach is the only one that consistently achieves high performance.
https://dl.acm.org/doi/abs/10.1145/3491102.3502000
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2022.acm.org/)