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Abstract: A novel couple images encryption algorithm based on DNA subsequence operation and chaotic system is presented. Different from the traditional DNA encryption methods, our algorithm is not use complex biological operation, but just uses the idea of DNA subsequence operation (such as elongation operation, truncation operation, and deletion operation). And then, do the DNA addition operation under the Chen's Hyper-chaotic map in this image cipher. The simulation experimental results and security analysis show that our algorithm not only has good encryption effect, but also has the ability of resisting exhaustive attack and statistical attack. [Copyright &y& Elsevier]

Abstract: In recent years, the studies of chaos-based image encryption have become an important aspect of the security of image transmission. So far, a large amount of chaotic maps have been used in image encryption with the permutation–diffusion architecture, such as logistic map, cat map, tent map and so on. In this paper, a defect of designing an image cryptosystem based on logistic map is proposed. According to the cryptanalysis of the defect, the initial value of logistic which is used to encrypt plain-image can be deduced by the decreased key space, when the parameter of logistic map is given. Finally, the theoretical analysis and numerical experimental results show that the defect could be used to break the image cryptosystem based on logistic map. [Copyright &y& Elsevier]

Abstract: A new image fusion encryption algorithm based on image fusion and DNA sequence operation and hyper-chaotic system is presented. Firstly, two DNA sequences matrices are obtained by encoding the original image and the key image. Secondly, using the chaotic sequences generated by Chen's hyper-chaotic maps to scramble the locations of elements from the DNA sequence matrix which generated form original image. Thirdly, XOR the scrambled DNA matrix and the random DNA matrix by using DNA sequence addition operation. At last, decoding the DNA sequence matrix, we will get the encrypted image. The simulation experimental results and security analysis show that our algorithm not only has good encryption effect, but also has the ability of resisting exhaustive attack and statistical attack. [Copyright &y& Elsevier]

Due to the efficient and secure requirements of image transmission, a number of research works have been done to encrypt the compressed image. Inspired by the arithmetic coding and chaotic map which are used to compress and encrypt image, respectively. In this paper, a scheme is proposed to encrypt the compressed image by chaotic map and arithmetic coding. This scheme compresses the image row by row which is firstly permuted by two logistic maps before arithmetic coding. It not only enhances the security of arithmetic coding, but also improves the compression ratio. To further improve the security of binary data which has been compressed, we use the chaotic maps to encrypt the data, and set different parameters and initial value for chaotic maps. In order to possess high sensitivities of key and plain-image, the keys that are employed to determine the parameter and initial value of chaotic maps are related to the plain-image. The experimental results validate the effect of the proposed scheme and demonstrate that the compressed and encrypted image is secure and convenient for transmission. [ABSTRACT FROM AUTHOR]

Face recognition is an important research hotspot. More and more new methods have been proposed in recent years. In this paper, we propose a novel face recognition method which is based on PCA and logistic regression. PCA is one of the most important methods in pattern recognition. Therefore, in our method, PCA is used to extract feature and reduce the dimensions of process data. Afterwards, we present a novel classification algorithm and use logistic regression as the classifier for face recognition. The experimental results on two different face databases are presented to illustrate the efficacy of our proposed method. [ABSTRACT FROM AUTHOR]

Abstract: Face recognition has become a research hotspot in the field of pattern recognition and artificial intelligence. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two traditional methods in pattern recognition. In this paper, we propose a novel method based on PCA image reconstruction and LDA for face recognition. First, the inner-classes covariance matrix for feature extraction is used as generating matrix and then eigenvectors from each person is obtained, then we obtain the reconstructed images. Moreover, the residual images are computed by subtracting reconstructed images from original face images. Furthermore, the residual images are applied by LDA to obtain the coefficient matrices. Finally, the features are utilized to train and test SVMs for face recognition. The simulation experiments illustrate the effectivity of this method on the ORL face database. [Copyright &y& Elsevier]