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首页> 外文期刊>The international arab journal of information technology >A Multimodal Biometric System Based on Palmprint and Finger Knuckle Print Recognition Methods
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A Multimodal Biometric System Based on Palmprint and Finger Knuckle Print Recognition Methods

机译：基于掌纹和指节指纹识别方法的多峰生物特征识别系统

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摘要

Biometric authentication is an effective method for automatically recognizing a person's identity. In our previous paper, we have considered palm print for human authentication. Recently, it has been found that the Finger Knuckle Print (FKP), which refers to the inherent skin patterns of the outer surface around the phalangeal joint of one's finger, has high capability to discriminate different individuals, making it an emerging biometric identifier. In this paper, the local convex direction map of the FKP image is extracted. Then, the local features of the enhanced FKP are extracted using the Scale Invariant Feature Transform (SIR), the Speeded Up Robust Features (SURF) and frequency feature. SIFT are formed by means of local patterns around key-points from scale space decomposed image. Feature vectors through SURF are formed by means of local patterns around key-points which are detected using scaled up filter. The frequency range of pixel levels in each image is employed by using Empirical Mode Decomposition (EMD). For the authentication of FKP image, we used shortest distance between the query image and the database, to evaluate their similarity. Here, we use PolyU FKP database images to examine the performance of the proposed system. The proposed biometric system is implemented in MATLAB and compared with the previous palm print human authentication system. For the same person, the matching score of the two methods are fused for the multimodal biometric recognition. The experimental results demonstrated the efficiency and effectiveness of this new biometric characteristic.
机译：生物特征认证是一种自动识别个人身份的有效方法。在我们以前的论文中，我们已经考虑过将掌纹用于人类认证。近来，已经发现指关节指印（FKP），指的是手指指关节周围的外表面的固有皮肤图案，具有区分不同个体的高能力，使其成为新兴的生物特征识别符。本文提取了FKP图像的局部凸方向图。然后，使用尺度不变特征变换（SIR），加速鲁棒特征（SURF）和频率特征提取增强的FKP的局部特征。 SIFT是通过比例空间分解图像中关键点周围的局部图案形成的。通过SURF的特征向量是通过关键点周围的局部图案形成的，这些局部点使用放大的滤波器进行检测。通过使用经验模式分解（EMD），可以使用每个图像中像素级别的频率范围。对于FKP图像的身份验证，我们使用查询图像与数据库之间的最短距离来评估它们的相似性。在这里，我们使用PolyU FKP数据库图像来检查所提出系统的性能。所提出的生物识别系统在MATLAB中实现，并与以前的掌纹人类认证系统进行了比较。对于同一个人，将两种方法的匹配分数融合在一起以进行多模式生物识别。实验结果证明了这一新生物特征的效率和有效性。

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