Augmenting remote multimodal person verification by embedding voice characteristics into face images

This paper presents a biometric watermarking algorithm to augment remote multimodal recognition by embedding voice characteristics into face images. We embed both a fragile watermark for tampering detection, as well as a robust watermark to represent the GMM parameters extracted from voice. We show that the proposed scheme can detect tampering, and is also robust to various watermarking attacks. Person verification experiments on the XM2VTS database indicate the validity of combining face and voice classifiers.

[1]  Jing Dong,et al.  Effects of watermarking on iris recognition performance , 2008, 2008 10th International Conference on Control, Automation, Robotics and Vision.

[2]  Douglas A. Reynolds,et al.  Robust text-independent speaker identification using Gaussian mixture speaker models , 1995, IEEE Trans. Speech Audio Process..

[3]  Nalini K. Ratha,et al.  An Analysis of Minutiae Matching Strength , 2001, AVBPA.

[4]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Richa Singh,et al.  Feature based RDWT watermarking for multimodal biometric system , 2009, Image Vis. Comput..

[6]  Andreas Uhl,et al.  Watermarking as a Means to Enhance Biometric Systems: A Critical Survey , 2011, Information Hiding.

[7]  Anil K. Jain,et al.  Hiding Biometric Data , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[9]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[10]  Robert I. Damper,et al.  Optimal weighting of bimodal biometric information with specific application to audio-visual person identification , 2009, Inf. Fusion.

[11]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[12]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.