Self-training-based face recognition using semi-supervised linear discriminant analysis and affinity propagation.
Self-training-based face recognition using semi-supervised linear discriminant analysis and affinity propagation.
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DOI：
10.1364/josaa.31.000001
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Haitao Gan;N. Sang;Rui Huang
中科院分区：
文献类型：
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作者：
Haitao Gan;N. Sang;Rui Huang
人脸识别是机器学习和计算机视觉最重要的应用之一。传统的监督学习方法需要大量的标记人脸图像才能达到良好的性能。然而，在实践中，标记的图像通常是稀缺的，而未标记的图像可能是丰富的。在本文中，我们介绍了一种半监督人脸识别方法，其中半监督线性判别分析（SDA）和亲和传播（AP）集成到一个自训练框架。特别地，SDA被用于使用标记和未标记的图像来计算人脸子空间，并且AP被用于识别子空间中的不同人脸类别的样本。然后可以根据样本对未标记的数据进行分类，并将具有最高置信度的新标记的数据添加到标记的数据中，整个过程迭代直到收敛。在四个人脸数据集上进行了一系列的实验，以评估我们的算法的性能。实验结果表明，我们的算法优于其他无监督，半监督和监督的方法。
Face recognition is one of the most important applications of machine learning and computer vision. The traditional supervised learning methods require a large amount of labeled face images to achieve good performance. In practice, however, labeled images are usually scarce while unlabeled ones may be abundant. In this paper, we introduce a semi-supervised face recognition method, in which semi-supervised linear discriminant analysis (SDA) and affinity propagation (AP) are integrated into a self-training framework. In particular, SDA is employed to compute the face subspace using both labeled and unlabeled images, and AP is used to identify the exemplars of different face classes in the subspace. The unlabeled data can then be classified according to the exemplars and the newly labeled data with the highest confidence are added to the labeled data, and the whole procedure iterates until convergence. A series of experiments on four face datasets are carried out to evaluate the performance of our algorithm. Experimental results illustrate that our algorithm outperforms the other unsupervised, semi-supervised, and supervised methods.