摘要
为了充分利用人脸的局部特征,提出了一种基于分块DCT和分块FLD相结合的人脸识别方法。该方法首先利用血流模型把红外温谱图转换成血流图,得到更加稳定的人脸生物特征。然后为了缓解小样本问题和充分利用图像的局部鉴别信息,对血流图做分块DCT和分块FLD,最后为了利用整体的统计特征,提出了基于欧式距离的Fisher加权准则,对分块特征加权得到最终的识别。实验结果表明,相比于传统的基于整体特征的识别方法(PCA+FLD),该方法得到了较好的识别结果。
To make full use of the local character and biological feature in human faces,a novel method for infrared face recognition based on blood perfusion is proposed in this paper.Firstly,thermal images are converted into blood perfusion domain by blood perfusion model to get the stable feature,which contains more local feature.Then,the block-DCT and block-FLD is chosen to get the local discrimination features from the statistical scope.Finally,the Fisher weighted criterion based on Euclidean distance is proposed to get the final results.The experiments illustrate that transformation from thermal images to blood perfusion domain can enlarge the ratio of between-class distance and within-class distance and the method proposed in this paper has better performance compared with traditional methods based on PCA and FLD.
出处
《武汉理工大学学报》
CAS
CSCD
北大核心
2010年第20期82-87,共6页
Journal of Wuhan University of Technology
基金
国家自然科学基金(60665001
10701040)
江西省教育厅科技项目(GJJD9296)
