Competition Code And LBP Palm Vein FeatureLevel Fusion Using Canonical Correlation Analysis 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 featurelevel 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.

Massive Fingervein Identification based on Local Line Binary Pattern under Parallel and Distributed Systems Massive individuals identification is a challenging problem in modern society. Particularly, fingervein recognition is an emerging biometric technique with several advantages, especially in terms of security against forgery. In this paper, we propose a hybrid parallel matching process for fingervein recognition under two different parallel platforms, by using the Local Line Binary Pattern descriptor and Hamming distance. Our proposal aims to reduce the computation time of the matching process for massive individuals identification by using fingervein patterns. Extensive evaluation shows that our approach obtains a high speedup under a multicore platform and a close to linear behavior for a multinode platform. The results with our hybrid parallel system show that it is suitable for realtime individuals identification, achieving a speedup up to 129.98x. To the best of our knowledge, our work is the first implementation of fingervein recognition under a parallel platform, which is the main contribution of this paper.