关键词: 二维局部保留映射, 支持向量机, 小样本, 掌纹识别

Abstract: The small sample biometrics recognition is a difficult problem in real-world applications because the limited training samples can not lead to satisfactory recognition accuracy.So in this paper，a novel method is proposed by using Improved two-Directional Local Preserving Projections（I2DLPP） for feature extraction and Support Vector Machine（SVM） for classification，in small sample palmprint recognition.I2DLPP，an improved algorithm of 2DLPP by projecting 2DPCA and 2DLPP in the row and column directions simultaneously to reduce the computation complexity and the final feature dimensions；and the nearest-neighbor graph is constructed in which each node corresponds to a column in the image matrix.SVM is proven to be an effective tool for small sample biometrics recognition.The combination of I2DLPP and SVM can improve recognition performance significantly for small sample palmprint recognition.The experimental results demonstrate the effectiveness of the proposed method.

Key words: two-Directional Local Preserving Projections（2DLPP）, Support Vector Machine（SVM）, small samples, palmprint recognition