Autocapture, Quality Assurance
Features & Functionality
- Includes C callable library or ActiveX control designed to be integrated into a larger application
- Includes source code to example programs
- 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 Microsoft Windows, Linux, and Solaris platforms
- Fully featured C Language API
- Example programs with source
- Java Native Interface support
- Microsoft Windows and Linux Support
Fingerprint Image Quality Analysis and Scoring Software
QualityCheck is an advanced fingerprint image quality scoring software library included in SequenceCheck and Aware WSQ1000 SDKs. QualityCheck uses advanced algorithms to assess whether a fingerprint image is of sufficient quality for biometric matching. QualityCheck implements a 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:
- image smudges due to movement, improper finger placement, or excess moisture
- image darkness due to excess pressure
- image lightness due to inadequate pressure
- miscalibrated sensor
- small image
- missing core or delta
- relative quality as compared to other images
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 indicates 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|
Typical Classification Thresholds
Quality Score Distribution
The correlation between the scores and the general quality of an image can be understood by examining the distribution curve shown in the graph below. Each of 17,000 FBI-compliant live-scanned images (different scanners, impressions, and rolls) are scored and plotted.
Minutiae and Core/Delta
QualityCheck 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.
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 scanned fingers that exhibited lower scores.