Block Pyramid Based Adaptive Quantization Watermarking for Multimodal Biometric Authentication

This paper proposes a novel robust watermarking scheme to embed fingerprint minutiae into face images for multimodal biometric authentication. First, a block pyramid is layered according to the block-wise face region distinctiveness estimated by Adaboost; upper level indicates informative spacial regions. Then, we adopt a first-order statics QIM method to perform watermark embedding in each pyramid level. Numeric watermark bits with higher priority are embedded into upper pyramid level with a larger embedding strength. By joint differentiation of host image regions and watermark bits priority, our scheme achieves a trade-offs among watermarking robustness, capacity and fidelity. Experimental results demonstrate that our approach guarantees the robustness of hidden biometric data, while preserving the distinctiveness of host biometric images.