Variability as a route to understanding face recognition

这个项目代表了一种看待人脸识别问题的新方法。尽管对这个话题进行了大量研究，但我们仍然不了解人脸处理的最基本方面：我们如何识别我们看到的人？这是人类感知中的一个关键问题，但它在法医和安全环境中也具有实际意义。这个项目源于一个简单的观察：同一张脸的照片看起来确实非常不同。在人脸识别的标准方法中，这一常见的事实被视为一种不便。同一个人的照片之间的差异被认为是“噪音”，要么被忽略，要么通过系统地控制用于研究的图像来消除。这项研究计划采取的恰恰是相反的方法。我们不会试图控制这种变异性，而是会对其进行明确的研究。在这种方法下，人脸识别的问题不是如何“区分人们”，而是如何“一起告诉人们”--如何将表面上不同的图像聚集在一起，形成一个连贯的表示。早期的工作表明，熟悉的人脸识别的一个非常重要的组成部分是能够概括表面上的图像差异--这些差异往往会愚弄不熟悉的观众，以及基于计算机的自动系统。目前未能解决这种差异可能是人脸识别进展缓慢的原因，这一进展落后于对人脸处理的其他方面的理解，如社会知觉。本研究由三部分组成。首先，将对同一人的图像之间的物理差异进行系统的检查。将统计技术应用于图形数据，目的是指定人脸图像的哪些方面有共同的变化，以及哪些方面对个人有特殊的变化。其次，一系列行为实验将检验我们对熟悉面孔的表征的性质--假设这种表征需要纳入变异性。第三，一系列研究将涉及人类观察者的实际人脸识别(例如，出于安全目的)。基于计算机的方法也将在这方面进行审查--尽管供应商声称这些系统仍然很差，但它们仍然很差。这种新的人脸识别方法有可能对近年来进展相当缓慢的领域做出重大贡献。

This project represents a new way to look at the problem of human face recognition. Despite a large amount of research on this topic, we still do not understand the most fundamental aspect of face processing: how can we identify the people we see? This is a key problem in human perception, but it also has practical implications in forensic and security settings. This project has its roots in a simple observation: pictures of the same face can look very different indeed. In the standard approach to face recognition, this commonplace fact is treated as an inconvenience. Differences between pictures of the same person are regarded as 'noise', and either ignored or eliminated by systematically controlling the images used for research. This research programme takes exactly the converse approach. Instead of trying to control away this variability, it will be studied explicitly. Under this approach, the problem of face recognition is not how to 'tell people apart', but instead how to 'tell people together' - how to bring together superficially different images into a coherent representation. Early work suggests that a very important component of familiar face recognition is the ability to generalize over superficial image differences - differences which tend to fool unfamiliar viewers, as well as automatic computer-based systems. The current failure to address this variability may account for the slow progress in face identification - progress which has fallen behind the understanding of other aspects of face processing such as social perception. This research comprises three components. First, a systematic examination will be conducted of the physical differences between images of the same person. Applying statistical techniques to graphical data, the aim is to specify what aspects of face images vary commonly, and what aspects vary idiosyncratically to that person. Second, a series of behavioural experiments will examine the nature of our representations of familiar faces - the hypothesis is that this representation needs to incorporate variability. Third, a series of studies will address practical face recognition by human observers (e.g. for security purposes). Computer-based approaches will also be examined in this strand - as these systems remain very poor, despite the claims of vendors. This novel approach to face identification has the potential to make a significant contribution to an area which has progressed rather slowly in recent years.