Actuation Confirmation and Negation via Facial-Identity and -Expression Recognition

A. L. Cheng, H. Bier, Galoget Latorre
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引用次数: 6

Abstract

This paper presents the implementation of a facial-identity and -expression recognition mechanism that confirms or negates physical and/or computational actuations in an intelligent built-environment. Said mechanism is built via Google Brain’s TensorFlow (as regards facial identity recognition) and Google Cloud Platform’s Cloud Vision API (as regards facial gesture recognition); and it is integrated into the ongoing development of an intelligent built-environment framework, viz., Design-to-Robotic-Production & -Operation (D2RP&O), conceived at Delft University of Technology (TUD). The present work builds on the inherited technological ecosystem and technical functionality of the Design-to-Robotic-Operation (D2RO) component of said framework; and its implementation is validated via two scenarios (physical and computational). In the first scenario—and building on an inherited adaptive mechanism—if building-skin components perceive a rise in interior temperature levels, natural ventilation is promoted by increasing degrees of aperture. This measure is presently confirmed or negated by a corresponding facial expression on the part of the user in response to said reaction, which serves as an intuitive override / feedback mechanism to the intelligent building-skin mechanism’s decision-making process. In the second scenario—and building on another inherited mechanism—if an accidental fall is detected and the user remains consciously or unconsciously collapsed, a series of automated emergency notifications (e.g., SMS, email, etc.) are sent to family and/or care-takers by particular mechanisms in the intelligent built-environment. The precision of this measure and its execution are presently confirmed by (a) identity detection of the victim, and (b) recognition of a reflexive facial gesture of pain and/or displeasure. The work presented in this paper promotes a considered relationship between the architecture of the built-environment and the Information and Communication Technologies (ICTs) embedded and/or deployed.
基于面部识别和表情识别的驱动、确认和否定
本文介绍了一种面部身份和表情识别机制的实现,该机制可以在智能建筑环境中确认或否定物理和/或计算驱动。该机制是通过Google Brain的TensorFlow(面部身份识别)和Google Cloud Platform的Cloud Vision API(面部手势识别)构建的;它被整合到智能建筑环境框架的持续发展中,即由代尔夫特理工大学(TUD)构思的设计到机器人生产和运营(D2RP&O)。目前的工作建立在继承的技术生态系统和该框架的设计到机器人操作(D2RO)组件的技术功能;其实现通过两个场景(物理和计算)进行验证。在第一种情况下(基于遗传的适应性机制),如果建筑表皮组件感知到室内温度水平的上升,则通过增加孔径来促进自然通风。这一措施目前通过用户对上述反应的相应面部表情来确认或否定,这是智能建筑皮肤机制决策过程的直观覆盖/反馈机制。在第二种情况下(基于另一种继承机制),如果检测到意外跌倒,并且用户有意识或无意识地保持崩溃状态,则智能建筑环境中的特定机制将向家人和/或看护人发送一系列自动紧急通知(例如SMS,电子邮件等)。这一措施的准确性及其执行目前通过(a)对受害者的身份检测和(b)对痛苦和/或不愉快的反射性面部手势的识别来证实。本文提出的工作促进了建筑环境的架构与嵌入和/或部署的信息和通信技术(ict)之间的考虑关系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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