多生物特征密码系统中基于面部和手掌静脉的模糊拱顶生成技术

N. Lalithamani, M. Sabrigiriraj
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引用次数: 5

摘要

生物识别系统的模板安全是一个重要的问题,需要重点关注。其重要性在于,与密码不同,被盗的生物识别模板无法撤销。因此,生物识别模板不能以普通格式存储,需要强有力的保护以防止任何伪造。本文提出了一种基于人脸和手掌静脉的模糊拱顶生成技术,用于多生物特征密码系统。在这里,首先使用各种过程对输入图像进行预处理,以使图像适合进一步处理。在该方法中,通过找出处理后的面部和手掌静脉图像的唯一共同点来提取特征。将箔条点与已提取的点相加,得到组合特征向量。将基于用户密钥输入生成的秘密密钥点加入到组合特征向量中,得到模糊vault。在解码过程中,构建手掌静脉和人脸图像的多模态生物特征模板,并与存储的模糊vault相结合生成最终密钥。最后,利用CASIA和JAFFE数据库中的掌纹和人脸数据库进行实验。采用的评估指标是FMR(假匹配率)和GMR(真匹配率)。从所提出系统的度量值中,我们可以推断出系统的性能良好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Technique to generate face and palm vein-based fuzzy vault for multi-biometric cryptosystem
Template security of biometric systems is a vital issue and needs critical focus. The importance lies in the fact that unlike passwords, stolen biometric templates cannot be revoked. Hence, the biometric templates cannot be stored in plain format and needs strong protection against any forgery. In this paper, we present a technique to generate face and palm vein-based fuzzy vault for multi-biometric cryptosystem. Here, initially the input images are pre-processed using various processes to make images fit for further processing. In our proposed method, the features are extracted from the processed face and palm vein images by finding out unique common points. The chaff points are added to the already extracted points to obtain the combined feature vector. The secret key points which are generated based on the user key input (by using proposed method) are added to the combined feature vector to have the fuzzy vault. For decoding, the multi-modal biometric template from palm vein and face image is constructed and is combined with the stored fuzzy vault to generate the final key. Finally, the experimentation is conducted using the palm vein and face database available in the CASIA and JAFFE database. The evaluation metrics employed are FMR (False Match Ratio) and GMR (Genuine Match Ratio). From the metric values obtained for the proposed system, we can infer that the system has performed well.
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来源期刊
Machine Graphics and Vision
Machine Graphics and Vision Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
0.40
自引率
0.00%
发文量
1
期刊介绍: Machine GRAPHICS & VISION (MGV) is a refereed international journal, published quarterly, providing a scientific exchange forum and an authoritative source of information in the field of, in general, pictorial information exchange between computers and their environment, including applications of visual and graphical computer systems. The journal concentrates on theoretical and computational models underlying computer generated, analysed, or otherwise processed imagery, in particular: - image processing - scene analysis, modeling, and understanding - machine vision - pattern matching and pattern recognition - image synthesis, including three-dimensional imaging and solid modeling
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