Dealing with the minutiae of fingerprint analysis

Wen Li and Srik Gurrapu, Texas Instruments

April 4, 2011

Wen Li and Srik Gurrapu, Texas Instruments

Fingerprint processing
After an image of a fingerprint is captured, a sequence of image processing algorithms will be applied to the captured image. In fingerprint authentication applications, two main types of technologies are being used: one is called minutiae based, and the other is called image based. This article presents the minutiae–based flow. The following sections will describe the main steps of the process as well as the key functions of each processing algorithms.

Minutiae are the special spots of a fingerprint that show the changing of the print. These spots have been predefined and categorized. Figure 4 shows two main features of minutiae, which are extracted from these spots:

• Ridge ending.
• Bifurcation.

Click on image to enlarge.

However, the minutiae are not limited to these two features.

In a minutiae-based system, the goal is to find the minutiae in the captured fingerprint image and compare them with fingerprints that are in the database. In order to extract the minutiae successfully, the fingerprint images must be preprocessed, which usually involves computationally-intensive image processing algorithms. The digital image signal processing steps include:

• Segmentation and filtering.
• Contrast enhancement.
• Orientation calculation.
• Gabor filtering.
• Binarization.
• Thinning.
• Feature extraction.

Segmentation and filtering. The main purpose of segmentation is to get the “good” area of a captured fingerprint image, then separate this valid fingerprint from the image background. Some filtering can be applied to the image to filter out the noise in the image.

Contrast enhancement. After segmentation, the image is subjected to gray stretch to increase the global contrast of the image. Because the skin of an entire finger has a similar color, the more interesting parts of the fingerprint and the less interesting areas have a very low level of contrast. During this step, the algorithm will stretch the gray levels of the ridges, short ridges, and bifurcation of the fingerprint and compress the gray levels of the less interesting parts of the fingerprint. Mathematically, this type of operation is transformation.

Orientation calculation. The ridges of fingerprint have a “directional” characteristic. Mapping out the orientation of a fingerprint’s ridges is essential for getting the valid minutiae. There are different implementations for mapping the orientation of a fingerprint. The most popular algorithm to map the orientation of fingerprints is the gradient-based approach.

The gradient ∇(x, y) at point [x,y] of I (an image) is a two-dimensional vector [∇x(x, y)∇y(x,y)].

Mathmatically, gradient ∇ is the first derivative of the image, the ∇x and ∇y are the derivatives on X and Y directions, respectively.

In a fingerprint system, to numerically calculate the ∇x(x, y)and ∇y(x,y), a popular method is to use the Sobel operator. The following is a 3x3 Sobel mask:



Click on image to enlarge.

The gradient can be calculated as following:


Click on image to enlarge.

Then the gradient’s direction, angle θ, can be calculated as:


Click on image to enlarge.

These calculations will be applied across the fingerprint image, and an orientation map will be created.

Gabor filtering. A Gabor filter is defined as a two-dimensional Gaussian function multiplied by a sinusoidal plane wave function:


Click on image to enlarge.

Here the x_θ and y_θ are the point coordinate [x,y] rotated (90-θ) degrees, defined as:


Click on image to enlarge.

Also, here:

• θ represents the orientation.
• f represents the frequency of the ridge-valley-ridge pattern; it can be the reciprocal of the width of ridge-valley measurement.
• σ_x and σ_y are the standard deviations of the Gaussian envelope on x and y directions, respectively.

Based on its definition, we can tell that the Gabor filtering is selective on both frequency and direction; this characteristic will dramatically enhance the fingerprint image. The certain desired frequencies of the image have been enhanced, and undesired noise has been removed. This leads to a robust image for reliable minutia-feature extracting.