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人脸图像中的噪声、腐蚀和变异会严重影响人脸识别系统的性能。为了提高系统的鲁棒性，提出了能够从噪声数据中学习的多类神经网络分类器。然而，在大的人脸数据集上，这样的系统不能提供高水平的鲁棒性。在本文中，我们探索了一个成对神经网络系统作为一种可供选择的方法来提高人脸识别的鲁棒性。实验结果表明，该方法在对受噪声影响的人脸图像的预测精度方面优于多类神经网络系统。
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英文标题：
《Comparing Robustness of Pairwise and Multiclass Neural-Network Systems
for Face Recognition》
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作者：
J. Uglov, V. Schetinin, C. Maple
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最新提交年份：
2007
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
Noise, corruptions and variations in face images can seriously hurt the performance of face recognition systems. To make such systems robust, multiclass neuralnetwork classifiers capable of learning from noisy data have been suggested. However on large face data sets such systems cannot provide the robustness at a high level. In this paper we explore a pairwise neural-network system as an alternative approach to improving the robustness of face recognition. In our experiments this approach is shown to outperform the multiclass neural-network system in terms of the predictive accuracy on the face images corrupted by noise.
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