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To solve the privacy image problem stored in cloud, we propose a compressive sensing (CS) based image compression, authentication, encryption algorithm in cloud, which can implement computation outsourcing to get rid of the expensive storage and computation costs. To achieve these goals, combined with the logistic-tent-sine chaotic map, a logistic-tent-sine chaotic map (LTSS) is proposed for it has larger chaotic ranges and better chaotic behaviors than simple 1D chaotic map such as a logistic chaotic map. Then, the LTSS chaotic system is employed for image protection algorithm in cloud, which contains three parts: data owner, cloud services and data user. The primary role of the data owner part is to compress and encrypt the image while embedding the owner’s Palmprint discriminative and binary features for authentication. The low-frequency part of the image is encrypted using the proposed Binary Data Cyclic Encryption algorithm (BDCE) while the owner’s palmprint features are embedded for authentication of the data owner. The high-frequency data are randomized for compression by the CS and following encrypted by the Double Random Phase Encoding (DRPE). For the cloud services, authentication services depend on the key and palmprint features and the high computational compressed sensing reconstruction is implemented in cloud can reduce the user burden. For data users, a user should provide the correct key and right palmprint, and then obtain an image available from the cloud service. The algorithm has better quality compared with the common compressive sensing based algorithms and robust to noise and occlusion for using the discrete wavelet transform. What is more, theoretical analysis and empirical evaluations show that the proposed algorithm can resist to exhaustive attack, differential attack and classical attacks.

To provide the necessary security and privacy privileges for registered users of biometric systems, a novel template protection method for generating cancelable palmprint features based on randomized cuckoo hashing and minHash is proposed in this paper. Firstly, the orthogonal features of palmprint are extracted using the anisotropic filter. Then, a novel randomized cuckoo hashing is applied on the binary palmprint feature as a means of first layer security. Randomized cuckoo hashing composes of two hash tables; before hashing, each column of original biometric template is filtered with a random matrix to improve discriminative ability. For the randomized cuckoo hashing with the same positions, we use different Gray coding methods instead, which improves irreversibility. Furthermore, to improve the efficiency and resist unlinkability attacks, another layer of privacy protection, MinHash is adopted. Experimental results on PolyU database show that our method can nearly preserve the original verification performance. The irreversibility, unlinkability and revocability properties are examined experimentally. Also, the privacy of palmprint protection scheme is analyzed under the Brute-force Attack, False Accept Attack and Birthday attack.

Coprophagy is of great significance to the growth, development, and reproductive performance of rabbits. This study is aimed at exploring the effect of coprophagy on the reproductive performance of New Zealand white rabbits by coprophagy prevention (CP). The results showed that CP treatment significantly decreased the growth and development performance of female rabbits and the live birth rate of embryos. The results of blood biochemical indexes showed that CP treatment significantly increased the contents of serum ALB, ALP, and MDA, while serum SOD activity was significantly decreased. Transcriptome analysis showed that GO terms were mainly enriched in transport function and reproductive function after CP treatment. In addition, KEGG results showed that inflammation related signal pathways were activated and the expression level of genes related to tight junction proteins was downregulated by CP treatment. Concurrently, western blot further confirmed the results of KEGG. In short, fecal feeding is an important survival strategy for some small rodents, coprophagy prevention will affect the inflammatory level of the body, change the oxidative stress level of the body, and then activate NOX4-ROS-NF-κB pathway, increase the expression level of adhesion protein ICAM-1 and VCAM-1, lead to the damage of uterine epithelial barrier, and then affect the reproductive performance of rabbits.

Based on singular value decomposition (SVD), an image compression, encryption, and identity authentication scheme is proposed here. This scheme can not only encrypt image data which would store in the cloud but also implement identity authentication. The authors use the SVD to decompose the image data into three parts: the left singular value matrix, the right singular value matrix, and the singular value matrix. The left singular value matrix and right singular value matrix are not as important as the singular value matrix. They propose a logistic-tent-sine chaotic system to encrypt them. In this scheme, they proposed a novel authentication value calculation algorithm, which can calculate the authentication value according to related data. According to the authentication value calculated from the ciphertext, the algorithm has the perfect authentication performance, so as in the scenarios if the image is cropped or added noisy. Theoretical analysis and empirical evaluations show that the proposed system can achieve better compression performance, satisfactory security performance, and low computational complexity.

In order to achieve biometric security and enhance privacy-preserving, a novel palmprint template protection scheme based on random comparison and noise data is proposed. Firstly, Anisotropic Filter (AF) is employed to capture the orientation information of the palmprint. Then, the orientation feature of palmprint is measured by a chaotic matrix to generate secure and cancelable palmprint template. The pseudo-randomness and non-ergodicity of the chaotic matrix can guarantee the security of template. Finally, in order to enhance the privacy protection of the template, the noise data with independent and identically distributed is added, as the final cancelable palmprint template. Theoretical analysis shows that a proper amount of noise has little effect on the recognition accuracy while the privacy is enhanced. During the matching stage, the recognition accuracy can be improved by fusing matching scores at the score-layer or the decision-layer. The theoretical effect of adding noise on the performance is also analyzed. The matching scores of the experimental results are consistent with the theoretical values, which means that we can reasonably adjusted the proportion of noise data through calculations and protect palmprint privacy on the basis of ensuring the recognition accuracy. Furthermore, our methods can still achieve very high security in the worst case of secret key theft.

The traditional Cancellable biometrics scheme needs another key or token to generate the revocable template, which usually suffers from the token-stolen problem. To solve this problem, a one-factor cancellable palmprint biometric recognition scheme based on Orthogonal Index of Maximum (OIOM) hash and Minimum Signature Hash (MSH) is proposed to generate pseudonymous identifier. Firstly, to improve the efficiency and effectiveness, a parallel structure is designed to obtain Orthogonal Gaussian random projection(GRP)matrices, which is employed to generate the OIOM hash code. Secondly, a random binary string is XOR the binary OIOM hash code to construct the helper data and it is stored in the database. Meanwhile, this string is hashed by MSH to get the final pseudonymous identifier. Lastly, during matching stage, based on helper data and a palmprint images, another pseudonymous identifier is generated to recognition. This implies that, just one factor, a palmprint of user, is needed in the matching stage, and therefore the privacy of user is preserved. To evaluate the proposed scheme, PolyU database and touchless TJU database are used in experiments. The noninvertible, renewability, unlinkability and several security attacks of the proposed scheme are analyzed. The experiment results and analysis show that the proposed scheme has strong security on the basis of maintaining palmprint recognition performance.

To extract more palmprint features and achieve better recognition results, an Orthogonal Index of Maximum and Minimum (OIOMM) revocable palmprint recognition method is proposed in this paper. Firstly, the competitive code features of region of interest (ROI) are extracted. Then, the statistical histogram of palmprint competition code features is obtained by partitioning the features. The Gaussian random projection (GRP)-based IOM mapping is used to generate a GRP matrix. Orthogonal GRP matrix is obtained by Schmidt orthogonalization. OIOMM hash converts real-valued biological eigenvectors into discrete index hash codes. Finally, the palmprint image is matched with Jaccard distance. The experiment is carried out in the palmprint database of Hong Kong Polytechnic University. When the random projection size is 200 and the revocable palmprint feature length is 500, the equal error rate is 0.90. This shows that the algorithm not only improves security but also maintains the classification effect.

We present a novel face recognition method based on direct discriminant Volterra kernels and effective feature classification (DD-VK). One of the crucial steps involves dividing face images into patches and using the DD-VK method to extract the features of sub-image patches. DD-VK implements diagonalization to discard useless information in the null space of the inter-class scatter matrix and preserve important discriminant information in the null space of the intra-class scatter matrix. This method can simultaneously maximize inter-class distances and minimize intra-class distances in the feature space. We also introduce a novel classification scheme associated with the 2D Volterra kernel feature. Our scheme aggregates the classification information obtained from each column of the feature matrix in each image patch and uses a voting strategy to implement parent face image classification. This procedure can reduce the influence of local negative information. Experimental results show that the proposed method demonstrates good performance when dealing with conventional face recognition problems and exhibits strong robustness when dealing with block occlusion images.

Dictionary learning is essentially a dimensionality reduction representation of large data sets and always attempts to learn the most pristine features behind the sample. In this paper, the palm vein feature is extracted with the Competitive code, then the Competition code feature of the palm vein is learned by a novel projective Dictionary Pair Learning (DPL) model for pattern classification tasks. Different from conventional Dictionary Learning (DL) methods, which learn a single synthesis dictionary, DPL learns jointly a synthesis dictionary and an analysis dictionary. Such a pair of dictionaries work together to perform representation and discrimination simultaneously. Compared with previous DL methods, DPL employs projective coding, which largely reduces the computational burden in learning and testing. The Competition code is used to extract the direction information of the palm vein. The above two methods are comprehensively applied to the palm veins. This paper uses the multispectral near-infrared palm vein image database of Hong Kong Polytech University for testing. Compared with the palm vein Competition code classification, the recognition rate of the palm vein Competition code feature learned by DPL is improved in some degree. When the palm vein features are extracted with filters in six different directions, the recognition rate of the palm vein Competitive code feature after learning with DPL is increased to 98.96%.

Information fusion mainly includes feature level fusion, matching-score level fusion and decision level fusion. However, the feature level fusion is considered to be a more effective fusion method because of the more biometric data than the matching fraction fusion and the decision level fusion. Feature level fusion is the extraction of feature information from the source image and the multiple features are analyzed, processed and integrated to get a single fusion image feature. In this paper, the palm vein features are extracted with Competition code and local binary pattern (LBP), respectively, to obtain two different palm vein features. Then two features are fused using discriminant correlation analysis (DCA). DCA is a feature level fusion technology, which associates class associations to the correlation analysis of feature sets. DCA implements effective feature fusion by maximizing the pairwise correlation between the two feature sets, and eliminating inter class correlation and restricting the correlation within the class. The Competition code uses the directional characteristics of the image to extract the palm vein features, while the LBP is an operator used to describe the local texture features of the image. The two features are complementary. Using DCA to combine the two characteristics of the palm vein can achieve a good classification effect. This paper uses the multispectral near-infrared palm vein image database of Hong Kong Polytech University for testing. Compared with the single palm vein Competition code feature or LBP feature, the DCA combines the two characteristics of the palm vein, which not only shortens the classification time in some degree, but also improves the recognition rate to 99.8% in the case of training samples of 9 and test samples of 3.

Biometrics is a discipline in computer science that uses biometrics to identify people and control access. With the development of technology, a variety of biometric recognition technologies are widely used, such as palm vein recognition technology. However, there are deficiencies in the feature extraction of the palm vein in one way. It is difficult to classify and identify. Therefore, the palm vein features are extracted in two different ways to obtain two different feature sets. And the two feature sets have complementary characteristics when expressing the palm vein features. Then, to improve the classification effect, we used the Canonical Correlation Analysis (CCA) to fuse the two feature sets. The palm vein features are extracted using a Competition Code and a Local Binary Pattern (LBP) to obtain two different palm vein features sets in this paper. The Competition Code uses the local orientation information of the image to extract the palm vein feature, and the LBP utilizes the local texture feature of the image to extract the palm vein feature. These two features can achieve complementarity. CCA is a feature-level fusion technique. CCA projects two feature sets into the same spatial domain through linear transformation, and achieves effective feature fusion in the same spatial domain. A good classification effect can be achieved by the two feature sets of the CCA fusion palm vein. Our experiments are carried out in a public database of Hong Kong Polytechnic University. Compared with the single palm vein competition code feature or LBP feature, the palm vein feature after CCA fusion shortens the classification time in some degree. And the recognition rate is increased to 98.14% when the ratio of the training sample to the test sample is 9:3.

Palmprint recognition has the advantages of high precision, fast speed, low price, easy to be accepted by users and so on.So it has a broad development prospect. As the palmprint image is easy to get blurred, it needs to be blurred before the recognition. In this paper, the recognition accuracy is improved by deblitting. On the basis of comparing a variety of defuzzification algorithms, the palmprint images nonlocally centralized sparse representation (PINCSR) is used to reconstruct the palmprint image. In the fact, it simulates that due to the defects of the palmprint acquisition device, resulting in the phenomenon of palmprint image blur. On the basis of palmprint image restoration, feature extraction is performed with a competition code and then classified based on the CR using a regularized least square method(CRC_RLS).Experimental results show that the algorithm has a good recognition effect.

In this paper, we proposed a joint and collaborative representation with Volterra kernel convolution feature (JCRVK) for face recognition. Firstly, the candidate face images are divided into sub-blocks in the equal size. The blocks are extracted feature using the two-dimensional Voltera kernels discriminant analysis, which can better capture the discrimination information from the different faces. Next, the proposed joint and collaborative representation is employed to optimize and classify the local Volterra kernels features (JCR-VK) individually. JCR-VK is very efficiently for its implementation only depending on matrix multiplication. Finally, recognition is completed by using the majority voting principle. Extensive experiments on the Extended Yale B and AR face databases are conducted, and the results show that the proposed approach can outperform other recently presented similar dictionary algorithms on recognition accuracy.

This paper introduces a simple and effective image encryption algorithm based on Compressive Sensing. Firstly, in order to obtain the sparse matrix of the plain image, we select the dual tree complex wavelet to transform the plain image into frequency domain and then we take noise shaping (NS) to make the sparse matrix coefficient more concentrated. Secondly, we use logistic and sine chaotic map system (LSS) to generate an encrypted measurement matrix. Finally, the sparse matrix and the encrypted measurement matrix are used to make a compression sensing operation. In order to improve the security of the proposed algorithm, we divide the resulting ciphertext into four parts and then use Arnold and LSS system (ALS) to encrypt them twice. Experiments and security analysis demonstrate the algorithm's excellent performance in image encryption and various attacks.

In order to provide privacy protection and security authentication for palmprint, a novel scheme for generating cancelable palmprint templates based on Bloom filters is proposed in this paper. Firstly, Gabor filters are used to extract CompCode from palmprint. Then, we propose a coding rule to encode CompCode blocks into transformation blocks. Nextly, these transformed blocks are mapped to the Bloom filters to form a cancelable palmprint features. Lastly, SVM classifiers are used for classification. The transformed blocks realize a layer of security protection. On the basis of the recognition rate, Boom filters provides effective protection of palmprint features. Experimental results on the Hong Kong PolyU Palmprint Database verify that the proposed cancelable scheme can achieve high recognition rate and protect palmprint templates at high security levels.

In this paper, we present a novel deep network model which is designed to deal with medical image super-resolution and has some resistance to noise contamination. We are mainly aimed at the medical image susceptible to noise contamination in the collection and transmission process, and the noise in medical images will be amplified after super-resolution reconstruction. We improve the Super-Resolution Convolution Neural Network (SRCNN) model mainly in two aspects. First, in order to make our model with noise resistance, we use discrete Harr wavelet transform as preprocessing algorithm. Second, we use adaptive partition algorithm based on image content to block the original image which can reduce the time complexity. The experimental results show that our model still achieves a good objective evaluation index (PSNR) and subjective visual effect on medical images that add Gaussian white noise. Our model is fast and effective and also have important guiding significance for the difficulty and risk assessment of surgical feasibility.

The traditional chaotic mapping encryption algorithm has the disadvantage of being vulnerable and insecure. Therefore, in order to improve the anti-attack capability and security of encryption system, an improved image encryption Method-plaintext association scrambling image encryption algorithm is proposed. The encryption process consists of two diffusion algorithms and a scrambling algorithm, and the inverse of the encryption process is the decryption process. The simulation results show that the system can obtain better encryption effect, and the security is higher than the traditional encryption algorithm.

In this paper, we proposed a palmprint template protection scheme based on matrix transformation. Firstly, the competition code features of original palmprint is extracted through the Gabor filters. Then, a general permutation matrix is generated randomly and two elementary permutation matrices are obtained by changing any two rows of it. Nextly, irreversible matrix is generated by XORing operation. Finally, cancelable palmprint templates are produced by multiplying the irreversible matrix and the original palmprint feature. Our experiments were carried out in a public database of Hong Kong Polytechnic University. The experimental results show that our cancelable palmprint scheme can not only ensure high safety but also meet the recognition accuracy requirements.

This paper presents a novel deep network model specifically for medical image super-resolution reconstruction. We take full advantage of the fact that all medical images basically have distinct repetitive structure and large black border without any texture information. Compared with the Super-Resolution Convolution Neural Network (SRCNN) structure, firstly, we add a convolution layer to carry out secondary feature extraction which make the feature more representative. Then we add overlapping pooling layers in order to highlight the important features and fine processing them. At last, in order to use local features and global features to complete the reconstruction together, we add a link layer, establish a connection between the second pooling layer and the reconstruction layer. The experimental results show that average PSNR gains achieved by our algorithm are higher than the original SRCNN. The reconstructed CT images can clearly provide important reference for clinicians to make the correct treatment decisions, and also have important guiding significance for the difficulty and risk assessment of surgical feasibility.

In order to enhance the classification accuracy of the two-dimensional feature of the image, the idea of a separate classification for each projection direction feature is proposed in this paper. Our method first divides the image into blocks and finds the two-dimensional sub-projection matrix of each sub-block, and then completes the feature extraction by using each column of the projection matrix. At the stage of feature classification, the feature of each projection direction of each sub-block is classified separately, cast a vote towards images classification and the result with the maximum votes wins. We named this method decision level vote classification (DL_VC). The proposed method is equivalent to transforming global features into local features, and the voting method can reduce the influence of local interference. Experiments on face databases demonstrate the efficacy of the proposed method.

To bridge the gap between the fuzziness of biometrics and the exactitude of cryptography, based on combining palmprint with two-layer error correction codes, a novel biometrics encryption method is proposed. Firstly, the randomly generated original keys are encoded by convolutional and cyclic two-layer coding. The first layer uses a convolution code to correct burst errors. The second layer uses cyclic code to correct random errors. Then, the palmprint features are extracted from the palmprint images. Next, they are fused together by XORing operation. The information is stored in a smart card. Finally, the original keys extraction process is the information in the smart card XOR the user’s palmprint features and then decoded with convolutional and cyclic two-layer code. The experimental results and security analysis show that it can recover the original keys completely. The proposed method is more secure than a single password factor, and has higher accuracy than a single biometric factor.

In order to achieve more secure and privacy-preserving, a new method of cancelable palmprint template generation scheme using noise data is proposed. Firstly, the random projection is used to reduce the dimension of the palmprint image and the reduced dimension image is normalized. Secondly, a chaotic matrix is produced and it is also normalized. Then the cancelable palmprint feature is generated by comparing the normalized chaotic matrix with reduced dimension image after normalization. Finally, in order to enhance the privacy protection, and then the noise data with independent and identically distributed is added, as the final palmprint features. In this article, the algorithm of adding noise data is analyzed theoretically. Experimental results on the Hong Kong PolyU Palmprint Database verify that random projection and noise are generated in an uncomplicated way, the computational complexity is low. The theoretical analysis of nosie data is consistent with the experimental results. According to the system requirement, on the basis of guaranteeing accuracy, adding a certain amount of noise will contribute to security and privacy protection.