Human behavior modeling publications

We present a series of two studies conducted to understand user's affective states during voice-based human-machine interactions.

In this paper we propose using simple temporal-CNN deep-learning models to assess student group collaboration that take in temporal representations of individual student roles as input.

We present a novel generative model for human motion modeling using Generative Adversarial Networks (GANs).

We hypothesized that, during sleep, children with OSAS have abnormal RREP, normal cortical processing of nonrespiratory stimuli, and persistence of abnormal RREP after treatment.

In this paper we present a closed loop mixed reality training system that provides automatic assessment of trainee performance during kinetic military exercises.

We present a novel approach for multi-modal affect analysis in human interactions that is capable of integrating data from multiple modalities while also taking into account temporal dynamics.

This study investigated whether improved recognition generalizes to novel faces, training effects are durable over 1 month, and baseline functioning levels predict the extent of improvement.

We present a completely automatic algorithm for initializing and tracking the articulated motion of humans using image sequences obtained from multiple cameras.

Occlusion and lack of visibility in crowded and cluttered scenes make it difficult to track individual people correctly and consistently, particularly in a single view. We present a multi-view approach…

We have developed a video fingerprinting system with robustness and efficiency as the primary and secondary design criteria.

In this paper, we presented a fully integrated real-time computer vision system that can detect and track multiple humans in a wide-area using a network of stereo cameras.