Fingerprint registration and verification is an active area of research in the field of image processing. Usually, fingerprints are obtained from sensors; however, there is recent interest in using images of fingers obtained from digital cameras instead of scanners. An unaddressed issue in the processing of fingerprints extracted from digital images is the angle of the finger during image capture. To match a fingerprint with 100% accuracy, the angles of the matching features should be similar. This paper proposes a rotation and scale-invariant decision-making method for the intelligent registration and recognition of fingerprints. A digital image of a finger is taken as the input and compared with a reference image for derotation. Derotation is performed by applying binary segmentation on both images, followed by the application of speeded up robust feature (SURF) extraction and then feature matching. Potential inliers are extracted from matched features by applying the M-estimator. Matched inlier points are used to form a homography matrix, the difference in the rotation angles of the finger in both the input and reference images is calculated, and finally, derotation is performed. Input fingerprint features are extracted and compared or stored based on the decision support system required for the situation.

中文翻译：

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一种基于数码相机的旋转不变指纹验证方法

指纹注册和验证是图像处理领域的一个活跃研究领域。通常，指纹是从传感器获取的；然而，最近人们对使用从数码相机而不是扫描仪获得的手指图像感兴趣。在处理从数字图像中提取的指纹时，一个未解决的问题是图像捕获期间手指的角度。要以 100% 的准确率匹配指纹，匹配特征的角度应该相似。提出一种旋转尺度不变的指纹智能配准与识别决策方法。将手指的数字图像作为输入并与参考图像进行比较以进行解旋。通过对两幅图像应用二进制分割来执行去旋转，其次是应用加速鲁棒特征（SURF）提取，然后是特征匹配。通过应用 M 估计器从匹配的特征中提取潜在的内点。匹配的内点用于形成单应矩阵，计算输入图像和参考图像中手指旋转角度的差异，最后进行反旋转。根据情况所需的决策支持系统提取和比较或存储输入的指纹特征。