A novel image hiding approach based on correlation analysis for secure multimodal biometrics
This paper proposes a novel multimodal biometric images hiding approach based on correlation analysis, which is used to protect the security and integrity of transmitted multimodal biometric images for network-based identification. Compared with existing methods, the correlation between the biometric images and the cover image is first analyzed by partial least squares (PLS) and particle swarm optimization (PSO), aiming to make use of the abundant information of cover image to represent the biometric images. Representing the biometric images using the corresponding content of cover image results in the generation of the residual images with much less energy. Then, considering the human visual system (HVS) model, the residual images as the secret images are embedded into the cover image using middle-significant-bit (MSB) method. Extensive experimental results demonstrate that the proposed approach not only provides good imperceptibility but also resists some common attacks and assures the effectiveness of network-based multimodal biometrics identification.
correlation analysis multimodal biometrics partial least squares particle swarm optimization middle-significant-bit