Gottschlich Carsten
Institute for Mathematical Stochastics, University of Göttingen, Goldschmidtstr. 7, 37077 Göttingen, Germany.
PLoS One. 2016 Feb 4;11(2):e0148552. doi: 10.1371/journal.pone.0148552. eCollection 2016.
We present a new type of local image descriptor which yields binary patterns from small image patches. For the application to fingerprint liveness detection, we achieve rotation invariant image patches by taking the fingerprint segmentation and orientation field into account. We compute the discrete cosine transform (DCT) for these rotation invariant patches and attain binary patterns by comparing pairs of two DCT coefficients. These patterns are summarized into one or more histograms per image. Each histogram comprises the relative frequencies of pattern occurrences. Multiple histograms are concatenated and the resulting feature vector is used for image classification. We name this novel type of descriptor convolution comparison pattern (CCP). Experimental results show the usefulness of the proposed CCP descriptor for fingerprint liveness detection. CCP outperforms other local image descriptors such as LBP, LPQ and WLD on the LivDet 2013 benchmark. The CCP descriptor is a general type of local image descriptor which we expect to prove useful in areas beyond fingerprint liveness detection such as biological and medical image processing, texture recognition, face recognition and iris recognition, liveness detection for face and iris images, and machine vision for surface inspection and material classification.
我们提出了一种新型的局部图像描述符，它可从小图像块中生成二进制模式。对于指纹活体检测应用，我们通过考虑指纹分割和方向场来实现旋转不变的图像块。我们对这些旋转不变块计算离散余弦变换（DCT），并通过比较两个DCT系数对来获得二进制模式。这些模式被总结为每个图像的一个或多个直方图。每个直方图包含模式出现的相对频率。多个直方图被连接起来，得到的特征向量用于图像分类。我们将这种新型描述符命名为卷积比较模式（CCP）。实验结果表明了所提出的CCP描述符在指纹活体检测中的有效性。在LivDet 2013基准测试中，CCP优于其他局部图像描述符，如LBP、LPQ和WLD。CCP描述符是一种通用的局部图像描述符，我们期望它在指纹活体检测之外的领域也能证明是有用的，如生物和医学图像处理、纹理识别、人脸识别和虹膜识别、面部和虹膜图像的活体检测以及用于表面检测和材料分类的机器视觉。
