摘要
极低分辨率图像本身包含的判别信息少且容易受到噪声的干扰,在现有的人脸识别算法下识别率较低。为了解决这一问题,提出一种基于图像超分辨率(SR)极限学习机(ELM)的人脸识别算法。首先,从样本库学习耦合的高低分辨率图像稀疏表达字典,利用高低分辨率表达系数的流形一致性重建高分辨率图像;其次,在超分辨率重建的高分辨率(HR)图像上构建ELM模型,训练获得前向神经网络的连接权值;最后,通过ELM预测输入极低人脸图像的类别属性。实验结果表明,针对于重建后的极低分辨率人脸图片,与协同表示的分类(CRC)人脸识别算法相比,所提算法将识别率分别提升了2%;同时也大幅度缩短了识别的时间。结果表明所提算法能够有效解决极低分辨率图片判决信息不足的问题,具有较好的识别能力。
The very low-resolution image itself contains less discriminant information and is prone to be interfered by noise, which reduces the recognition rate of the existing face recognition algorithm. In order to solve this problem, a very low resolution face recognition algorithm via Super-Resolution（ SR） based on Extreme Learning Machine（ ELM） was proposed.Firstly, the sparse expression dictionary of Low-Resolution（ LR） and High-Resolution（ HR） images were learned from sample base, and the HR image could be reconstructed due to the manifold consistency of LR and HR expression coefficients.Secondly, the ELM model was built on the HR reconstructed images, the connection weight of feedforward neural networks was obtained by training. Lastly, the ELM was used to predict the category attribute of the very low-resolution image. Compared with traditional face recognition algorithm based on Collaborative Representation Classification（ CRC）, the experimental results show that the recognition rate of the proposed algorithm increases by 2% upon the reconstructed HR images. At the same time, it greatly shortens the recognition time. The simulation results show that the proposed algorithm can effectively solve face recognition problem caused by limited discriminant information in very low-resolution image and it has better recognition ability.
出处
《计算机应用》
CSCD
北大核心
2016年第2期580-585,共6页
journal of Computer Applications
基金
国家863计划项目(2013AA12A202)
国家自然科学基金资助项目(61172173
61502354)
国家留学基金管理委员会项目
湖北省自然科学基金资助项目(2012FFA099
2012FFA134
2013CF125
2014CFA130
2015CFB451)
湖北省教育厅重点科研项目(D20141505)
武汉工程大学科学研究基金资助项目(K201403)~~
关键词
稀疏表达
超分辨率
极限学习机
极低分辨率
人脸识别
sparse representation
Super-Resolution（SR）
Extreme Learning Machine（ELM）
very low resolution
face recognition
