Palm-Dorsa Vein Recognition Based on Two- Dimensional Fisher Linear Discriminant
In Fisher Linear Discriminant (FLD), the within-class scatter matrix is always singular. To overcome the above problem and preserve discriminatory information, a new method for palm-dorsa vein feature extraction based on Two-Dimensional FLD (2DFLD) is presented in this paper. We applied PCA, PCA+FLD and 2DFLD to extract the palm-dorsa vein feature subspace. The images to be recognized were projected onto the low-dimensional subspace. A classifier to vein matching based on cosine distance was used. Experimental results suggested that the recognition rate of PCA+FLD is about 4.84％ higher than that of PCA. Compared with PCA+FLD, 2DFLD is able to yield recognition rate as high as 98.44％, with accuracy enhanced by 7.51％, while the feature extraction time is only 0.4 s. It was demonstrated that the algorithm is effective and quick.
palm-dorsa vein recognition Principal Components Analysis (PCA) Fisher Linear Discriminant (FLD) Two-Dimensional FLD(2DFLD)
Jing Liu Yue Zhang
School of Information Engineering, Shenyang University of Chemical Technology, SYUCT, Shenyang, Chin Investigation and Design Institute of Water Resources and Hydropower Liaoning Province, IDIWRHLN She
国际会议
武汉
英文
550-552
2011-10-21（万方平台首次上网日期，不代表论文的发表时间）