A Watermarking Technique Using Discrete Curvelet Transform for Security of Multiple Biometric Features

TL;DR

This paper presents a biometric watermarking method using Discrete Curvelet Transform that embeds multiple biometric features into images to enhance security and prevent spoofing, with features extracted via Shen-Castan and PCA.

Contribution

It introduces a novel multi-biometric watermarking technique embedding fingerprint, face, iris, and signature features into curvelet coefficients for improved security.

Findings

Effective embedding of multiple biometric features into images.

Watermarked images resist illegal removal of biometric features.

Suitable for multi-factor biometric authentication and copyright protection.

Abstract

The robustness and security of the biometric watermarking approach can be improved by using a multiple watermarking. This multiple watermarking proposed for improving security of biometric features and data. When the imposter tries to create the spoofed biometric feature, the invisible biometric watermark features can provide appropriate protection to multimedia data. In this paper, a biometric watermarking technique with multiple biometric watermarks are proposed in which biometric features of fingerprint, face, iris and signature is embedded in the image. Before embedding, fingerprint, iris, face and signature features are extracted using Shen-Castan edge detection and Principal Component Analysis. These all biometric watermark features are embedded into various mid band frequency curvelet coefficients of host image. All four fingerprint features, iris features, facial features and signature features are the biometric characteristics of the individual and they are used for cross verification and copyright protection if any manipulation occurs. The proposed technique is fragile enough; features cannot be extracted from the watermarked image when an imposter tries to remove watermark features illegally. It can use for multiple copyright authentication and verification.