面向人脸识别的判别低秩字典学习算法

人脸识别是计算机视觉和模式识别领域的一个研究热点，有着十分广泛的应用前景.人脸识别任务在训练样本和测试样本同时包含噪声的情况下存在识别精度不高的问题，为此本文提出一个新的判别低秩字典学习和低秩稀疏表示算法（Discriminative Low-Rank Dictionary Learning for Low-Rank Sparse Representation，DLRD_LRSR）.本文方法在模型中约束每个子字典和稀疏表示低秩避免噪声干扰，并引入了判别重构误差项增强系数的判别性.为验证算法的有效性，本文在3个公开人脸数据集上进行了实验评估，结果表明与现有字典学习算法相比，本文算法能够更好的解决训练样本和测试样本同时存在噪声的人脸识别问题.

Discriminative Low-Rank Dictionary Leaning For Face Recognition

Face recognition is active in the field of computer vision and pattern recognition and has extremely wide-spread application prospect. However, the problem that both training images and testing images are corrupted is not well solved in face recognition task. To address such a problem, this paper proposes a novel Discriminative Low-Rank Dictionary Learning for Low-Rank Sparse Representation algorithm (DLRD_LRSR) aiming to learn a pure dictionary. We suggest each sub dictionary and sparse representation be low-rank for reducing the effect of noise in training samples and introduce a novel discriminative reconstruction error term to make the coefficient more discriminating. We demonstrate the effectiveness of our approach on three public face datasets. Our method is more effective and robust than the previous competitive dictionary learning method.