Information maximization in face processing
Information maximization in face processing
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DOI：
10.1016/j.neucom.2006.02.025
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发表时间：
2007-08-01
期刊：
影响因子：
6
通讯作者：
Bartlett, Marian Stewart
中科院分区：
文献类型：
--
作者：
Bartlett, Marian Stewart
这篇观点论文探讨了无监督学习的原理以及它们与人脸识别的关系。依赖性编码和信息最大化似乎是视觉系统早期神经编码的核心原则。这些原则可能与我们如何思考高级视觉过程（如面部识别）有关。本文首先回顾了生物视觉中依赖学习的例子，以及最优信息传递和信息最大化的原理。接下来，我们从信息最大化的角度研究了计算机人脸识别算法。特征脸方法可以看作是一种学习人脸图像像素间一阶和二阶依赖关系的无监督系统。特征面只有在输入分布为高斯分布的情况下才能使信息传输最大化。独立成分分析（ICA）除了学习一阶和二阶关系外，还学习高阶依赖关系，并为更一般的输入分布集最大化信息传输。基于ICA的人脸表征比特征脸具有更好的识别性能，这支持了信息最大化是人脸识别等高级视觉功能的好策略的理论。最后，我们回顾了依赖性学习与人脸感知相关的知觉研究，并提出了非典型化偏见和人脸适应后效等知觉效应的信息最大化解释。(C) 2007 Elsevier B.V.版权所有
This perspective paper explores principles of unsupervised learning and how they relate to face recognition. Dependency coding and information maximization appear to be central principles in neural coding early in the visual system. These principles may be relevant to how we think about higher visual processes such as face recognition as well. The paper first reviews examples of dependency learning in biological vision, along with principles of optimal information transfer and information maximization. Next, we examine algorithms for face recognition by computer from a perspective of information maximization. The eigenface approach can be considered as an unsupervised system that learns the first- and second-order dependencies among face image pixels. Eigenfaces maximize information transfer only in the case where the input distributions are Gaussian. Independent component analysis (ICA) learns high-order dependencies in addition to first- and second-order relations, and maximizes information transfer for a more general set of input distributions. Face representations based on ICA gave better recognition performance than eigenfaces, supporting the theory that information maximization is a good strategy for high level visual functions such as face recognition. Finally, we review perceptual studies suggesting that dependency learning is relevant to human face perception as well, and present an information maximization account of perceptual effects such as the atypicality bias, and face adaptation aftereffects. (C) 2007 Elsevier B.V. All rights reserved.