Aware’s QualityCheck is an advanced fingerprint image quality scoring software library included in Aware's WSQ1000 SDK. QualityCheck uses complex algorithms to assess the quality and usefulness of a fingerprint image to help improve the overall performance of an AFIS.  QualityCheck implements a non-AFIS specific measure of finger image quality that is based on the continuity of ridge flow across all regions of a finger image, and returns information based on the following factors:

1. Is the image smudged due to movement, improper finger placement, or excess moisture?

2. Is the image too dark due to excess finger pressure applied to the livescan platen or due to miscalibration of the sensor?

3. Is the image too light due to inadequate finger pressure applied to the livescan platen or due to miscalibration of the sensor?

4. Is the image too small?

5. Does the image lack a core or delta?

6. Given a large population of similar FBI compliant finger images, how does this image compare?

Functionality
QualityCheck generates an overall score between 0 and 100, and provides information on areas of the image that exhibit problems. These areas are returned to a software application as arrays of pixel regions or as a color-coded image of the finger, which indicate the specific problems with the finger image. This functionality can improve the ability of an operator to screen bad images.

The finger images shown below, in order from best to worst quality, are samples from field deployed systems. The quality values and color coding information are returned by the Aware QualityCheck functions. The color codes provide quick visual assistance to identify the following gross problems with an image:

Blue = smudged or broken areas
Red = areas that are too dark
Yellow = areas that are too light
Green = areas of good quality

Example Classification Thresholds:

0-40 Poor
41-60 Marginal
61-75 Adequate
76-100 Good

Minutiae and Core/Delta
Helps identify partial images or images consisting only of fingertips. Image #7 shows an example where the minutiae count is low, and no core or delta was found. Partial finger images can pose a particular problem because they may have good ridge flow, but still do not provide the correct information. Lack of core/delta and low minutiae counts helps to flag those images.

Number of Good Pixels
Provides the total count of the green area for each image. This is the part of the image where Minutiae points likely can be extracted from. This number can be used to flag images that are too small.

Number of Bad Pixels
Provides the total count of the red (too dark), yellow (too light) and blue (broken, smudged) pixels. Images with low ratios of good-to-bad pixels (images #4, 5, 6, and 7) closely correlate with low quality scores.

Q Percentile
Describes where the given image falls in the sample distribution shown in the plot below. The value indicates the percent of images from this database of 17,000 FBI-compliant livescanned fingers that exhibited lower scores. 

• Includes C callable library or ActiveX control designed to
  be integrated into a larger application
• Includes example programs and their  source code
• Provides a score indicating quality of finger ridge data
• Manual and auto image cropping functions to remove the finger
  ridge data from a noisy or large background image
• Provides minutiae counts and number of core/delta found
• Indicates pixel regions that are good, too dark, too light, or that have
  smudged/broken finger ridges
• Color-coded image-based function returns this same information
• Usable with all matchers and systems
• Statistically reliable results
• Supported by MS Windows, Linux, and Solaris platforms
  

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