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The major objective in developing a robust digital watermarking algorithm is to obtain the highest possible robustness without losing the visual imperceptibility. To achieve this objective, we proposed in this paper an optimal image watermarking scheme using multi-objective particle swarm optimization (MOPSO) and singular value decomposition (SVD) in wavelet domain. Having decomposed the original image into ten sub-bands, singular value decomposition is applied to a chosen detail sub-band. Then, the singular values of the chosen sub-band are modified by multiple scaling factors (MSF) to embed the singular values of watermark image. Various combinations of multiple scaling factors are possible, and it is difficult to obtain optimal solutions. Thus, in order to achieve the highest possible robustness and imperceptibility, multi-objective optimization of the multiple scaling factors is necessary. This work employs particle swarm optimization to obtain optimum multiple scaling factors. Experimental results of the proposed approach show both the significant improvement in term of imperceptibility and robustness under various attacks.

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Abstract. Human recognition technology based on biometrics has received increasing attention over the past decade. Iris recognition is considered to be the most reliable biometric authentication system and is becoming the most promising technique for high security. In this paper, we propose a multiscale approach for iris localization by using wavelet modulus maxima for edge detection, a fast and a compact method for iris feature extraction based on wavelet maxima components and moment invariants. The features are represented as feature vector, thus allowing us to also propose a fast matching scheme based on exclusive OR operation. Experimental results have shown that the performance of the proposed method is very encouraging and comparable to the well known methods used for iris texture analysis.

Iris recognition is one of the most reliable personal identification methods and is becoming the most promising technique for high security. In this paper, we propose an efficient method for personal iris identification by investigating iris textures that have a high level of stability and distinctiveness. To improve the efficiency and accuracy of the proposed system, we present a new approach to making a feature vector compact and efficient by using wavelet transform (wavelet maxima components), and moment invariants. The proposed scheme is invariant to translation, rotation, and scale changes. Experimental results have shown that the proposed system could be used for personal identification in an efficient and effective manner.Index Terms-multiscale edge detection, iris feature extraction, wavelet maxima, moment invariants.