Human emotion recognition based on remote video sequencing

情绪建模与识别已引起心理学等学科的广泛关注， 认知科学，以及最近的工程学。这项奖励的目标是开发和测试 多伦多大学的一种基于视频的自动人类情感识别系统，可以部署在 灵活且具有成本效益的方式。支持公司Archith Labs开发了这样的方法 用于视频序列的情感分析，可以揭示用户的情感状态。然而，有意义的是 需要在各种设备上进行无处不在的企业级部署。这给我们带来了新的挑战 情感识别系统，因为识别必须在不受控制的照明和姿势下执行 条件以及真实世界环境的其他限制。 我们将重点介绍的具体应用是开发情感识别(ER)方法 将受众参与度与教育应用程序的沉闷程度进行分类。设想的系统将能够 为教师(或演讲者)提供实时的观众参与度反馈。有各种各样的 人们可以使用的模式，但本质上，心率，瞳孔眨眼，也许凝视可以传达 这个应用程序的有趣信息。所有数据将通过视频排序远程收集，使用 架构师的ER技术和UofT的情感计算和视频处理专业知识。

Emotion modeling and recognition has drawn extensive attention from disciplines such as psychology, cognitive science, and, lately, engineering. The objective of this Engage grant is to develop and test at the University of Toronto an automated video based human emotion recognition system that can be deployed in a flexible and cost effective manner. The supporting company Architech Labs has developed such methodologies for affective analysis of video sequences that can reveal the user's emotional state. However, there is significant demand for ubiquitous, enterprise level deployment on a variety of devices. This presents a new challenge for emotion recognition systems since recognition has to be performed with uncontrolled lighting and pose conditions as well as other restrictions of a real world setting. The specific application that we will focus on is developing emotion recognition (ER) methodologies to classify audience engagement vs boredom for educational applications. The envisioned system would be able to provide a teacher (or a presenter) with real -time audience engagement feedback. There are all sorts of modalities that one can use for that but, in essence, the heart rate, pupil blinks and perhaps gaze could convey interesting information for this application. All data will be collected remotely by video sequencing using Architech's ER technologies and UofT's affective computing and video processing expertise.