Abstract—The use of biometric data to increase the robustness and security of private data has been mentioned and studied extensively. The multiple watermarking approach has proposed in this paper aims to improve the security of biometric data features; we consider using multiple watermarking techniques with fingerprint, face, iris and signature features. Before embedding, the fingerprint feature extracted by Minutiae with Gabor filter enhancement. Iris feature is extracted by Daugman Gabor filter. Face and signature features are extracted through a Gabor filter that combines PCA. Then the features of Iris and Fingerprint are mixed together, called iris-finger. And facial and signature features are also mixed together in terms of coefficients, called face-sig. The iris-finger feature set is embedded in the curvelet coefficients at level 1. The face-sig feature set is embedded in curvelet coefficients at level 2. All features of the fingerprint, iris, face and signature are used for authentication and copyright protection if there are attacks. the results have been also compared to others approaches.
Index Terms—biometric features, multilevel discrete curvelet transform, contourlet transform, multiple watermarking, daugman gabor filter, gabor wavelet, minutiae, PCA
Cite: Hoan Nguyen-Thanh, Thuong Le-Tien, and Thang Nguyen-Duy, "Multiple Watermarking with Biometric Data Using Discrete Curvelets and Contourlets," Journal of Image and Graphics, Vol. 6, No. 2, pp. 122-126, December 2018. doi: 10.18178/joig.6.2.122-126
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