Collaborative representation Bayesian face recognition



近年来, 基于协同表示的人脸识别方法取得了诸多进展。然而, 在训练集字典欠完备的小样本情况下, 基于协同表示的分类方法的结果并不理想。这很大程度上, 是由于该方法使用了欧式距离残差判据。传统的欧式距离残差判据在小样本情况下, 并不能有效地区分类内残差和类间残差。针对这一问题, 我们引入了贝叶斯残差模型来更好地区分类内残差, 并将协同表示机制与贝叶斯残差模型结合, 提出了协同表示贝叶斯人脸识别方法。实验证明, 我们提出的方法在小样本情况下具有比较明显的优势, 且该方法具有较好的可迁移性。创新点:

  1. 1、

    分析了基于协同表示分类方法的残差模型, 并指出了传统的欧式距离残差模型在小样本情况下的问题。

  2. 2、

    引入了贝叶斯残差模型来更好地区分小样本情况下的类内残差和类间残差, 并提出了使用类内灰度差来估计类内残差分布的方法。

  3. 3、

    将协同表示模型与贝叶斯残差模型相结合, 提出了协同表示贝叶斯人脸识别方法。改方法在小样本情况下具有较好的鲁棒性, 并具有较好的可迁移性。

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This work was supported by National Natural Science Foundation of China (Grant No. 61333015) and National Basic Research Program of China (973) (Grant No. 2011CB302400).

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Correspondence to Xiao Ma.

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The authors declare that they have no conflict of interest.

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Feng, J., Ma, X. & Zhuang, W. Collaborative representation Bayesian face recognition. Sci. China Inf. Sci. 60, 048101 (2017).

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  • 脸识别
  • 小样本问题
  • 协同表示
  • 贝叶斯残差模型
  • 类内差
  • 类间差