Feature based RDWT watermarking for multimodal biometric system
Introduction
Biometric authentication systems have inherent advantage over traditional personal identification techniques [21]. However, there are many critical issues in designing a practical biometric system. These issues are broadly characterized by accuracy, computation speed, cost, security, scalability and real time performance. The security of biometric data is of paramount importance and must be protected from external attacks and tampering [20]. Ratha et al. [25] characterized common attacks in biometric systems as coercive attack, impersonation attack, replay attack, and attacks on feature extractor, template database, matcher, and matching results. Attacks can alter the contents of biometric images or templates and can degrade the performance of a biometric system. It is therefore required to protect the biometric templates of individuals at all times.
Researchers have proposed algorithms to handle challenges confronted by security of biometric systems. Encryption is one way of addressing this issue and has been discussed in [10], [30], [33]. Another way of securing biometric images and templates is by watermarking. Recently, researchers have proposed algorithms based on image watermarking techniques to protect biometric data [13], [19], [20], [25], [34]. In biometric watermarking, a certain amount of information referred to as watermark, is embedded into the original cover image using a secret key, such that the contents of the cover image are not altered. Some of these methods perform watermarking in the spatial domain [13], [19], [20] while other methods embed the biometric watermark in the frequency domain [25], [34]. In existing biometric watermarking algorithms the cover image is either gray scale face image or fingerprint image, and the watermark data is fingerprint minutiae information [20] or face information [19] or iris codes [34].
In this paper we propose a novel biometric watermarking algorithm to securely and robustly embed the biometric voice template into the color face image of the same individual. Color face image is used as the host image and Mel Frequency Cepstral Coefficients (MFCC) extracted from the voice data are used as watermark. Face and voice are chosen for watermarking because of the widespread application of face and speaker verification. There are several applications where either face or voice or both are used to authenticate an individual [14]. The proposed watermarking algorithm first computes the embedding capacity and location in the face image using edge and corner phase congruency method [22]. Embedding and extraction of voice data is based on Redundant Discrete Wavelet Transformation [9]. The performance of the proposed watermarking algorithm is validated using face, voice and multimodal verification algorithms. We observe that the proposed watermark embedding and extraction algorithm does not affect the quality of the original face image or the recognition performance. In addition, the proposed algorithm is robust and resilient to common attacks. We perform statistical evaluations to further validate that the proposed watermarking algorithm does not affect the verification performance of biometric watermark and cover image.
Section 2 in the paper presents the proposed biometric watermarking algorithm. Section 3 describes the database and recognition algorithms used for verifying the integrity of the biometric data. Section 4 describes the computation of user-specific parameters for the proposed watermarking algorithm and Section 5 discusses the experimental results in detail.
Section snippets
Proposed biometric watermarking algorithm
Usually, image watermarking is performed using Discrete Wavelet Transform (DWT) because DWT preserves frequency information in stable form and allows good localization both in time and spatial frequency domain [9], [26], [31]. However, one of the major drawbacks of DWT is that the transformation does not provide shift invariance because of the downsampling of its bands. This causes a major change in the wavelet coefficients of the image even for minor shifts in the input image. In watermarking,
Verifying the integrity of the extracted biometric data
To validate the performance of the proposed biometric watermarking algorithm, experiments are performed with the color face image and MFCC matrix computed from voice signal of the same individual. The MFCC watermarked face image is stored in the database for recognition. For verification, the MFC coefficients are extracted from the watermarked face image. The extracted MFC coefficients and the face image are matched with the query voice data and face image.
In general watermarking algorithms,
Computing the biometric watermarking parameters for optimal performance
In this section, we describe the process for computing the parameters involved in the proposed RDWT biometric watermarking. These parameters are computed to obtain the optimal face, voice and multimodal verification performance. The parameters that affect the performance of RDWT biometric watermarking algorithm are as follows:
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α1 and α2 control the strength of the watermark MFCCs during embedding and extraction.
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Parameter a in Eq. (2) controls the visual quality of the watermarked face image.
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n
Experimental validation
Section 5.1 experimentally substantiates the benefits of RDWT over DWT for the proposed watermarking approach. Section 5.2 extends the experimental results of RDWT watermarking by computing the verification performance of face, voice and multimodal biometrics for different attacks on the watermarked face image. This experiment is performed to verify the integrity and robustness of the proposed biometric watermarking algorithm. Section 5.3 experimentally validates the need for embedding the
Conclusion
With the increased use of biometric systems, the possibility of attacks on the biometric images and templates also increases. In this paper, we proposed a feature based watermarking algorithm to protect the biometric templates in a multimodal biometric system. Using Redundant Discrete Wavelet Transform, the voice coefficients are embedded into the color face image while preserving the facial features. The robustness of the watermarking algorithm is evaluated by comparing the recognition
Acknowledgement
Authors would like to acknowledge Dr. M. Tistarelli and Dr. J. Bigun for their valuable suggestions. Authors thank the reviewers for their helpful and constructive comments. This research (Award No. 2003-RC-CX-K001) was supported by the Office of Science and Technology, National Institute of Justice, United States Department of Justice.
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