Guang Feng

Super-Resolution Based on Noise Resistance Deep Convolutional Network

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.

Joint and collaborative representation with local Volterra kernels convolution feature for face recognition

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.

Face recognition based on Volterra kernels direct discriminant analysis and effective feature classification

Abstract 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.

A Privacy-preserving Cancelable Palmprint Template Generation Scheme Using Noise Data

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.

Biometrics encryption combining palmprint with two-layer error correction codes

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.

Fisher Discriminant Analysis with L1-Norm for Robust Palmprint Recognition

In this paper, a linear discriminant analysis method with L1 norm (LDA-L1) for palmprint recognition is proposed. The traditional linear discriminant analysis method based on L2 norm is very sensitive to outliers, but the L1 specification can overcome this problem very well. In the LDA-L1 method, a series of projection vectors are obtained by the iterative method, which can maximize the inter class dispersion and minimize the L1 norm based on within class dispersion. We tested the performance of our approach in the PolyU palmprint database. The experimental results show that LDA-L1 has robustness to outliers.

Palm-print recognition algorithm based on random projection and chaotic system

To improve the efficiency and security of palm-print recognition, we proposed a palm-print algorithm which based on random projection and chaotic system. Using twice random projections based on chaotic system to process the palm-print image, it generated the random project matrix and palm-print which are equivalent to two-factor identity authentications. And the initial value of chaotic system also could be the key to protect the security of palm-print. Reducing the dimensionality of the transformation matrix which could be produced randomly and the transformation matrix was not depend on the original training sample completely. In addition, the process of producing the random project matrix is simple and has lower computational complexity. And users could change the key by regain the new random project matrix to protect their privacy.

Sparse Representation Based on Discriminant Locality Preserving Dictionary Learning for Face Recognition

A novel discriminant locality preserving dictionary learning (DLPDL) algorithm for face recognition is proposed in this paper. In order to achieve better performance and less computation, dimensionality reduction is applied on original image samples. Most of the proposed dictionary learning methods learn features and dictionary, however, the inner structure of feature is hardly considered. Therefore, by incorporating discriminant locality preserving criteria into dictionary learning, the margin of coefficients distance between between-class and within-class is encourage to be large in order to enhance the classification ability and gain discriminative information. What is more, the local structure of the feature is also preserved, which is very vital in face recognition performance. Our experiments on Extended Yale B, AR and CMU face database demonstrated the proposed algorithm has higher recognition performance than other dictionary learning based classification methods.

Direct Discriminant Analysis Using Volterra Kernels for Face Recognition

Based on non-linear Volterra kernels mapping and direct discrimination analysis(DD-Volterra), a novel face recognition algorithm is proposed. Firstly, the original image is segmented into specific sub blocks and seeks functional mapping using truncated Volterra kernels. Next, simultaneous diagonalization obtain Volterra kernel optimal projection matrix. This matrix can discard useless information that exist in the null space of the inter-class. Also, it can reserve discriminative information that exist in the null space of the intra-class. Finally, in the test, each block of the test image is classified separately, voting strategy and nearest neighbor classifier algorithm are used for classification. Experiments show that the proposed DD-Volterra method has better performance for it is more effective than Volterrafaces during the extracting facial feature stage.