摘要
提出了一种基于稀疏分解的不同光照和姿态的人脸识别方法。通过给定的样本为每一类人脸图像训练一个特定的字典,使得在稀疏限制条件下,图像的表示误差最小。将测试图像投影到每一个字典的原子所形成的空间,然后利用误差向量进行分类。为了处理不同光照和姿态问题,采用了基于反照率估计的姿态的重照技术产生同一个人的不同光照条件下的多幅正面图像,从而使得本文方法能够在只有极少数训练图像的条件下获得很高的识别率。通过采用公用数据库中的人脸图像进行验证表明本文方法能够有效的实现不同光照和姿态条件下的人脸识别,其在性能方面比现有大多数方法更优。
A face recognition algorithm based on simultaneous sparse approximations under varying illumination and pose is proposed. A dictionary is learned for each class based on given training examples which minimizes the representation error with a sparseness constraint. A novel test image is projected onto the span of the atoms in each learned dictionary and the resulting residual vectors are then used for classification. To handle variations in lighting conditions and pose, an image relighting technique is used to generate multiple frontal images of the same person with variable lighting. As a result, the proposed algorithm has the ability to recognize human faces with high accuracy even when only a single or a very few images per person are provided for training. The efficiency of the proposed method is demonstrated using publicly available databases and it is shown that this method is efficient and can perform significantly better than many competitive face recognition algorithms.
出处
《科技通报》
北大核心
2015年第5期204-209,260,共7页
Bulletin of Science and Technology
基金
广东省自然科学基金资助项目(NO.S2013010012920)
关键词
字典学习
人脸识别
光照变化
dictionary learning
face recognition
illumination variation
