In this work we apply a computationally efficient, closed form design of a jointly optimized filter bank of correlation filter classifiers for biometric verification with the use of multiple biometrics from individuals. Advanced correlation filters have been used successfully for biometric classification, and have shown robustness in verifying faces, palmprints and fingerprints. In this study we address the issues of performing robust biometric verification when multiple biometrics from the same person are available at the moment of authentication; we implement biometric fusion by using a filter bank of correlation filter classifiers which are jointly optimized with each biometric, instead of designing separate independent correlation filter classifiers for each biometric and then fuse the resulting match scores. We present results using fingerprint and palmprint images from a data set of 40 people, showing a considerable advantage in verification performance producing a large margin of separation between the impostor and authentic match scores. The method proposed in this paper is a robust and secure method for authenticating an individual.
This paper introduces an application of steganography for hiding cancelable biometric data based on quad-phase correlation filter classification. The proposed technique can perform two tasks: (1) embed an encrypted (cancelable) template for biometric recognition into a host image or (2) embed the biometric data required for remote (or later) classification, such as embedding a transformed face image into the host image, so that it can be transmitted for remote authentication or stored for later use. The novel approach is that we will encode Fourier information of the template (or biometric) in the spatial representation of the host image. More importantly we show that we only need two bits per pixel in the frequency domain to represent the filter and biometric, making it compact and ideal for application of data hiding. To preserve the template (or biometric) from vulnerabilities to successful attacks, we encrypt the filter or biometric image by convolving it with a random kernel which produces an image in the spatial domain that resembles white noise, thus essentially both the frequency and spatial representation will have no visible exploitable structure. We also present results on reduced complexity correlation filter classification performance when using biometric images recovered from stego-images.