Multi-view Representations for Pose Invariant Face Recognition in Man and Machine

人类有一种难以置信的能力，可以识别熟悉的人，尽管视觉画面发生了很大的变化。这些变化包括光线、视角、距离、遮挡物和眼镜、头发等周边元素的变化，以及由于言语和表情而导致的面部静态和动态形状变化。尽管人类具有这种高度功能性的能力，但面部外观的变化对计算机视觉系统构成了重大挑战。在计算机视觉系统中实现姿态不变人脸识别（PIFR）仍然是实现人脸识别作为一种被动生物识别技术的全部潜力的一个重要障碍。在人工智能的发展过程中，pose-invariant人脸识别问题已经得到了广泛的解决，但它仍然是一个重要的障碍。PIFR是由人类视觉系统毫不费力地实现的，但目前我们对人类系统的了解还不够好，无法为这些明显的技术挑战提供和实施合理的解决方案。因此，这个博士学位的目的将是加强我们对人类观察者如何实现面部姿势不变识别的理解，以便为人工智能策略提供信息。我们将特别关注多视图或姿势感知策略，并将这些与基于对象的模型或姿势不可知方法进行比较。这项工作将包括与人类参与者进行广泛的心理物理实验和探索姿势不变动态人脸识别的计算机模型的计算实验。心理学和心理物理学实验将包括行为研究，以及结合功能磁共振成像（fMRI）等神经成像方法的研究，以帮助了解与人体姿势不变性有关的大脑网络。这些实验将包括确定人类观察者是否以及如何利用或忽视视觉输入中的变化，同时实现姿势不变的面部识别。

Humans have an incredible ability to identify familiar individuals despite substantial changes in the visual picture. These can include changes in lighting, viewing angle, distance, occluders and peripheral elements such as glasses and hair, and both static and dynamic shape change of the face due to speech and expression. Despite this highly functional human capability, facial appearance variations pose a significant challenge to computer vision systems. Achieving Pose-Invariant Face Recognition (PIFR) in computer vision systems remains a significant stumbling block to realizing the full potential of face recognition as a passive biometric technology. Extensive efforts have been made to try to solve the problem of pose-invariant face recognition in the development Artificial Intelligence, yet it remains a significant barrier. PIFR is achieved effortlessly by the human visual system but at present we do not understand the human system well enough to provide and implement plausible solutions to the clear technological challenges. The aim of this PhD studentship will therefore be to enhance our understanding of how human observers achieve pose invariant recognition of faces in order to inform AI strategies. We will particularly focus on multi-view or pose-aware strategies and compare these against object-based models or pose-agnostic approaches. The work will involve both extensive psychophysical experimentation with human participants and computational experiments exploring computer models of pose-invariant dynamic face recognition. The psychological and psychophysical experimentation will include behavioural studies as well as studies incorporating neuroimaging methods such as functional magnetic resonance imaging (fMRI) to help understand the brain networks involved in pose invariance in humans. These experiments will include determining whether, and how, human observers utilise or disregard the variations in the visual input whilst achieving pose invariant face recognition.