Fingerprint Smear Detection Based on Subband Feature Representation
Fingerprint Smear Detection Based on Subband Feature Representation
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DOI：
10.1155/2011/412647
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发表时间：
2011-01-01
影响因子：
1.9
通讯作者：
Yang, Xiukun
中科院分区：
文献类型：
--
作者：
Yang, Xiukun
由于指纹涂抹组织的不稳定纹理及其与正常手指区域的相似性，指纹涂抹检测已经成为一个具有挑战性的问题。提出了一种结合对称小波变换、灰度共生矩阵和DCT变换的指纹图像污点检测方法。首先提出了一种特征提取算法，利用SWT分解每个指纹和表征局部纹理特征的缺陷手指组织的SWT系数在子带4类似于19。在特征向量中引入了基于并发矩阵的纹理特征，进一步提高了纹理分类的灵敏度。然后将连接的特征向量馈送到预先训练的遗传神经网络分类器中，该分类器通过将指纹子块标记为不同类别来识别涂抹。最后，DCT分解被用来检测指纹图像中的小污点区域和突然断裂的异常。实验结果表明，该混合方法可以有效地识别各种类型的指纹涂抹。
Fingerprint smear detection has become a challenging issue due to the erratic texture of the smear tissue and its similarity to normal finger area. This paper presents a novel fingerprint image smear detection approach integrating symmetric wavelet transform (SWT), gray level co-occurrence matrix, and DCT. A feature extraction algorithm is first proposed by utilizing SWT to decompose each fingerprint and characterizing local texture features of defective finger tissue with the SWT coefficients in subbands 4 similar to 19. Concurrence matrix-based texture features are incorporated into the feature vector to further improve the texture classification sensitivity. The concatenated feature vector is then fed into a pretrained genetic neural network classifier, which identifies smears by labeling fingerprint subblocks into different categories. Finally, DCT decomposition is used to detect abnormalities in fingerprint images containing small smear areas and abrupt breakages. Experimental results indicate that the hybrid method can effectively identify various types of fingerprint smears.