NIST Fingerprint Image Registration Library (NFRL). Registers a pair of fingerprint images using two pairs of control-points (pixel locations). Registration is rigid; translation and rotation are performed without scaling.

NFRL registers two fingerprint images based on a pair of corresponding control-points. It uses this control-point
pair of pixel locations within the images to translate and rotate the Moving image to the Fixed image.
Runtime configuration parameters include:
* moving and fixed image data in 8-bits-per-pixel grayscale (preferable but not required)
* two-pairs of corresponding control points (pixel coordinates).
The fingerprint-image rigid-registration process is performed in two steps:
1. Translation of the Moving image to the Fixed image using the "first" pair of control-points (the unconstrained pair)
2. Rotation of the Moving image around the Fixed image control-point (the translation "target" location) based on the
angle-difference determined by the "second" pair of control-points (the constrained pair).
Both final images, a few interim images, and registration metadata generated during the registration process
are made available to the using software:
* Final registered Moving image
* Final registered Fixed image
* Registered, padded, overlaid image (colorized)
* Registered, padded, Moving image (grayscale)
* Padded, Fixed image (grayscale)
* Summed, registered, dilated overlaid image (the "blob")
* Process metadata available in both text and XML format.
The two Final images are registered. They are cropped to the region-of-interest that is the smallest area of "overlap"
per the registration. Therefore, these two images have identical width and height which enables analysis using metrics
like PSNR (Peak Signal to Noise Ratio).

NFRL系统基于一对对应控制点注册两张指纹图像。该系统利用图像中的像素坐标控制点对，对动态图像进行平移和旋转，以匹配静态图像。运行时配置参数包括：
* 动态和静态图像数据以每像素8位灰度格式（推荐但非必需）；
* 两对对应控制点（像素坐标）。指纹图像的刚性配准过程分为两个步骤：
1. 使用“第一对”控制点（非约束对）将动态图像平移至静态图像；
2. 根据由“第二对”控制点（约束对）确定的角差，围绕静态图像的控制点（平移目标位置）旋转动态图像。
在配准过程中生成的最终图像、若干中间图像和配准元数据均可供使用软件获取：
* 最终注册的动态图像；
* 最终注册的静态图像；
* 注册、填充、叠加的图像（彩色）；
* 注册、填充、动态图像（灰度）；
* 填充的静态图像（灰度）；
* 求和、注册、膨胀叠加的图像（“blob”）；
* 可在文本和XML格式中获取的过程元数据。
两个最终图像已进行配准，并裁剪至配准的最小重叠区域，因此这两个图像具有相同的宽度和高度，这有助于使用如PSNR（峰值信噪比）等指标进行分析。