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首页> 外文期刊>Procedia Computer Science >Feature Vector Extraction based Texture Feature using Hybrid Wavelet Type I & II for Finger Knuckle Prints for Multi-Instance Feature Fusion
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Feature Vector Extraction based Texture Feature using Hybrid Wavelet Type I & II for Finger Knuckle Prints for Multi-Instance Feature Fusion

机译：基于特征向量提取的纹理特征，使用混合小波类型I和II用于指节指纹，用于多实例特征融合

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摘要

The finger knuckle print (FKP) of a particular person is found to be unique and can serve as a biometric feature has been revealed recently by the researchers. In this research Finger Knuckle Print has been used as a biometric feature. Hybrid Wavelet Type I and Hybrid Wavelet Type II were used for feature extraction from the images in order to process it further. The important role of hybrid wavelet transform is to combine the key features of two different orthogonal transforms so that the strengths of both the transform wavelets are used. In this research the different transforms like (Discrete Cosine Transform) DCT, Haar. Hartley, Walsh and Kekre are used in combination for generation of 20 different hybrid wavelets. These hybrid wavelets are applied on the database images to generate feature vector coefficients and they are then subjected to Intra Class testing And Inter Class Testing and their performance is evaluated and compared. Proposed system has given up to 80% of EER for TAR-TRR (PI) for hybrid wavelet formed using (Discrete Cosine Transform) DCT and Kekre transform for the multimodal multi-instance implementation.
机译：研究人员最近发现，特定人的指关节指纹（FKP）是唯一的，并且可以用作生物特征。在这项研究中，指关节指纹已被用作生物特征。混合小波I型和混合小波II型用于从图像中提取特征，以便对其进行进一步处理。混合小波变换的重要作用是将两个不同的正交变换的关键特征组合在一起，从而利用两个变换小波的强度。在这项研究中，不同的变换像（离散余弦变换）DCT，Haar。 Hartley，Walsh和Kekre结合使用可生成20个不同的混合小波。将这些混合小波应用于数据库图像以生成特征矢量系数，然后对其进行类内测试和类间测试，并评估和比较它们的性能。对于使用多模态多实例实现的（离散余弦变换）DCT和Kekre变换形成的混合小波，拟议的系统已放弃TAR-TRR（PI）的EER的80％。

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