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 today’s information age, people will depend more and more on computers to obtain and make use of information, there is a big gap between the multimedia information after digitization that has large data and the current hardware technology that can provide the computer storage resources and network band width. For example, there is a large amount of image storage and transmission problem. Image compression becomes useful in cases when images need to be transmitted across networks in a less costly way by increasing data volume while reducing transmission time. This paper discusses image compression to effect on face recognition system. For compression purposes, we adopted the JPEG, JPEG2000, JPEG XR coding standard. The face recognition algorithms studied are SIFT. As a form of an extensive research, Experimental results show that it still maintains a high recognition rate under the high compression ratio, and JPEG XR standards is superior to other two kinds in terms of performance and complexity.
Based on multiple maximum scatter difference discrimination Dictionary learning, a novel face recognition algorithm is proposed. Dictionary used for sparse coding plays a key role in sparse representation classification. In this paper, a multiple maximum scatter difference discriminated criterion is used for dictionary learning. During the process of dictionary learning, the multiple maximum scatter difference computes its discriminated vectors from both the range of the between class scatter matrix and the null space of the within-class scatter matrix. The proposed algorithm is theoretically elegant and easy to calculate. Extensive experimental studies conducted on the AR database and Extended Yale Database B in comparison with existing basic sparse representation and other classification methods, it shows that the performance is a little better than the original sparse representation methods with lower complexity.
In this paper, we propose a novel palmprint recognition algorithm. Firstly, the palmprint images are represented by
the anisotropic filter. The filters are built on Gaussian functions along one direction, and on second derivative of
Gaussian functions in the orthogonal direction. Also, this choice is motivated by the optimal joint spatial and frequency
localization of the Gaussian kernel. Therefore,they can better approximate the edge or line of palmprint images. A
palmprint image is processed with a bank of anisotropic filters at different scales and rotations for robust palmprint
features extraction. Once these features are extracted, subspace analysis is then applied to the feature vectors for
dimension reduction as well as class separability. Experimental results on a public palmprint database show that the
accuracy could be improved by the proposed novel representations, compared with Gabor.
Proc. SPIE. 9067, Sixth International Conference on Machine Vision (ICMV 2013)
KEYWORDS: Facial recognition systems, FDA class I medical device development, Detection and tracking algorithms, Databases, Error analysis, Computer programming, Image classification, Convolution, Reconstruction algorithms, Scene classification
Sparse representation based classification (SRC) could not well classify the sample belonging to different classes distribute on the same direction. To solve the problem, a Volterra kernel sparse representation based classification (Volterra-SRC) algorithm is proposed in this paper. Firstly, the original face images are divided into non overlapped patches and then mapped into a high dimensional space by utilizing the Volterra kernels. During the training stage, following by the Fisher criteria, the objective function is defined to obtain the optimal Volterra kernels via maximizing inter-class distances and minimizing intra-class distances simultaneously. During the testing stage, a voting procedure is introduced in conjunction with a sparse representation based classification to decide to which class each individual patch belongs. Finally, the aggregate classification results of all patches in a face are used to determine the overall recognition outcome for the given face image. We demonstrate the experiments on ORL and Extended Yale B benchmark face databases and show that our proposed Volterra-SRC algorithm consistently outperforms the original SRC and the proposed has some advantages and robustness in case of small train number samples.