The finger joint lines defined as finger creases and its distribution can identify a person. In this paper, we propose a new finger crease pattern recognition method based on Legendre moments and principal component analysis （PCA）. After obtaining the region of interest （ROI） for each finger image in the pre- processing stage, Legendre moments under Radon transform are applied to construct a moment feature matrix from the ROI, which greatly decreases the dimensionality of ROI and can represent principal components of the finger creases quite well. Then, an approach to finger crease pattern recognition is designed based on Karhunen-Loeve （K-L） transform. The method applies PCA to a moment feature matrix rather than the original image matrix to achieve the feature vector. The proposed method has been tested on a database of 824 images from 103 individuals using the nearest neighbor classifier. The accuracy up to 98.584% has been obtained when using 4 samples per class for training. The experimental results demonstrate that our proposed approach is feasible and effective in biometrics.
In this paper, the idea of interest coverage is provided to form clusters in sensor network, which mean that the distance among data trends gathered by neighbor sensors is so small that, in some period, those sensors can be clustered, and certain sensor can be used to replace the cluster to form the virtual sensor network topology. In detail, the Jensen-Shannon Divergence (JSD) is used to characterize the distance among different distributions which represent the data trend of sensors. Then, based on JSD, a hierarchical clustering algorithm is provided to form the virtual sensor network topology. Simulation shows that the proposed approach gains more than 50% energy saving than Sta- tistical Aggregation Methods (SAM) which transmitted data gathered by sensor only when the differ- ence among data exceed certain threshold.
