A Multitask Learning Approach to Face Recognition Based on Neural Networks

机译：基于神经网络的多任务人脸识别方法

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摘要

For traditional human face based biometrics, usually one task (face recognition) is learned at one time. This single task learning (STL) approach may neglect potential rich information resources hidden in other related tasks, while multitask learning (MTL) can make full use of the latent information. MTL is an inductive transfer method which improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias. In this paper, backpropagation (BP) network based MTL approach is proposed for face recognition. The feasibility of this approach is demonstrated through two different face recognition experiments, which show that MTL based on BP neural networks is more effective than the traditional STL approach, and that MTL is also a practical approach for face recognition.

机译：对于传统的基于人脸的生物特征识别，通常一次学习一项任务（人脸识别）。这种单任务学习（STL）方法可能会忽略隐藏在其他相关任务中的潜在丰富信息资源，而多任务学习（MTL）可以充分利用潜在信息。 MTL是一种归纳传递方法，它通过将相关任务的训练信号中包含的域信息用作归纳偏置来提高通用性。本文提出了一种基于BP网络的MTL人脸识别方法。通过两个不同的人脸识别实验证明了该方法的可行性，这表明基于BP神经网络的MTL比传统的STL方法更有效，并且MTL也是一种实用的人脸识别方法。

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来源
《Intelligent Data Engineering and Automated Learning - IDEAL 2008》|2008年|24-31|共8页
会议地点 Daejeon(KR);Daejeon(KR)
作者
Feng Jin; Shiliang Sun;
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作者单位

Department of Computer Science and Technology, East China Normal University, Shanghai 200241, P.R. China;

Department of Computer Science and Technology, East China Normal University, Shanghai 200241, P.R. China;

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会议组织
原文格式 PDF
正文语种 eng
中图分类计算机网络;
关键词
multitask learning (MTL); single task learning (STL); face recognition; backpropagation (BP); artificial neural network (ANN);

机译：多任务学习（MTL）；单任务学习（STL）；人脸识别;反向传播（BP）；人工神经网络（ANN）;

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A Multitask Learning Approach to Face Recognition Based on Neural Networks

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