Vatsa Mayank, Singh Richa, Noore Afzel
Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506-6109, USA.
Int J Neural Syst. 2007 Oct;17(5):343-51. doi: 10.1142/S0129065707001196.
This paper proposes an intelligent 2nu-support vector machine based match score fusion algorithm to improve the performance of face and iris recognition by integrating the quality of images. The proposed algorithm applies redundant discrete wavelet transform to evaluate the underlying linear and non-linear features present in the image. A composite quality score is computed to determine the extent of smoothness, sharpness, noise, and other pertinent features present in each subband of the image. The match score and the corresponding quality score of an image are fused using 2nu-support vector machine to improve the verification performance. The proposed algorithm is experimentally validated using the FERET face database and the CASIA iris database. The verification performance and statistical evaluation show that the proposed algorithm outperforms existing fusion algorithms.
本文提出了一种基于智能2nu支持向量机的匹配分数融合算法，通过整合图像质量来提高面部和虹膜识别的性能。该算法应用冗余离散小波变换来评估图像中存在的潜在线性和非线性特征。计算一个综合质量分数，以确定图像每个子带中存在的平滑度、清晰度、噪声和其他相关特征的程度。使用2nu支持向量机融合图像的匹配分数和相应的质量分数，以提高验证性能。利用FERET面部数据库和CASIA虹膜数据库对该算法进行了实验验证。验证性能和统计评估表明，该算法优于现有的融合算法。
