A novel image hiding approach based on correlation analysis for secure multimodal biometrics

  • Authors:

  • Miao Qi;Yinghua Lu;Ning Du;Yinan Zhang;Chengxi Wang;Jun Kong

  • Affiliations:

  • Computer School, Northeast Normal University, Changchun, China and Faculty of Chemistry, Northeast Normal University, China;Faculty of Chemistry, Northeast Normal University, China;Computer School, Northeast Normal University, Changchun, China;Computer School, Northeast Normal University, Changchun, China;Computer School, Northeast Normal University, Changchun, China;Computer School, Northeast Normal University, Changchun, China and Key Laboratory for Applied Statistics of MOE, Northeast Normal University, China

  • Venue:
  • Journal of Network and Computer Applications
  • Year:
  • 2010

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Abstract

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.