A new method for dorsal hand vein pattern recognition is presented in the paper. To improve the recognition ratio, the vein skeleton extracting with little distortion is very important. Firstly, our method acquires a clean, skeleton with little distortion after a series of processes: size and gray normalizing, Gaussian low pass and wiener filtering, adaptive thresholding segmenting, area thresholding, morphological opening and closing, conditional thinning, spurs pruning. Then, the seven corrected moment invariants of the vein skeleton are extracted as the feature vector. At last, the feature vector is input into KNN for training and recognition. Experiment shows the algorithm achieves a higher recognition ratio of 97.75%.

Vein pattern recognition; Preprocessing; Segmenting; Feature extraction; Nearest Neighbor Classifier (KNN)

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