Native Quality Measures

While NFIQ2 produces a unified quality score (1-5), it also computes numerous native quality measures that can provide detailed insights into specific aspects of fingerprint image quality.

Overview

Native quality measures are the individual quality features computed from fingerprint images before being combined into a unified score. These measures include:

FDA: Frequency Domain Analysis
OCL: Orientation Certainty Level
RVUP: Ridge Valley Uniformity
LCS: Local Clarity Score
OF: Orientation Flow
Mu: Mean intensity
MMB: Minutiae-based measures
And many more…

Computing Quality Measures

There are two approaches to obtain native quality measures:

Approach 1: Direct Computation

Compute quality measures directly from an image:

#include <nfiq2.hpp>

// Create fingerprint image data
NFIQ2::FingerprintImageData rawImage = NFIQ2::FingerprintImageData(
    data.get(), cols * rows, cols, rows, 0, 500);

// Compute native quality measures
std::unordered_map<std::string, double> qualityFeatures;
try {
    qualityFeatures = NFIQ2::QualityMeasures::
        computeNativeQualityMeasures(rawImage);
} catch (const NFIQ2::Exception &e) {
    std::cerr << "Error: " << e.what() << '\n';
    return EXIT_FAILURE;
}

// Access individual measures
for (const auto &feature : qualityFeatures) {
    std::cout << feature.first << ": " << feature.second << '\n';
}

Approach 2: From Algorithm Objects

If you’ve already computed algorithms for the unified score, extract measures from them:

// Step 1: Compute algorithm objects
std::vector<std::shared_ptr<NFIQ2::QualityMeasures::Algorithm>> algorithms;
try {
    algorithms = NFIQ2::QualityMeasures::
        computeNativeQualityMeasureAlgorithms(rawImage);
} catch (const NFIQ2::Exception &e) {
    std::cerr << "Error: " << e.what() << '\n';
    return EXIT_FAILURE;
}

// Step 2: Extract quality measures
std::unordered_map<std::string, double> qualityFeatures =
    NFIQ2::QualityMeasures::getNativeQualityMeasures(algorithms);

// Step 3: Use for unified score (avoiding recomputation)
unsigned int nfiq2 = model.computeUnifiedQualityScore(algorithms);

Approach 2 is more efficient when you need both the unified score and individual measures, as it avoids recomputing the quality algorithms.

Listing Available Measures

Get all available quality measure identifiers:

// Get all native quality measure IDs
std::vector<std::string> featureIDs =
    NFIQ2::QualityMeasures::getNativeQualityMeasureIDs();

std::cout << "Available quality measures:\n";
for (const auto &featureID : featureIDs) {
    std::cout << "  - " << featureID << '\n';
}

Example output:

Available quality measures:
  - EmptyImageOrContrastTooLow
  - UniformImage
  - FingerprintImageWithMinutiae
  - SufficientFingerprintForeground
  - FDA_Bin10_Mean
  - FDA_Bin10_StdDev
  - FingerJetFX_MinutiaeCount
  - FJFXPos_Mu_MinutiaeQuality_2
  - OCL_Bin10_Mean
  - RVUP_Bin10_Mean
  - ...

Measuring Computation Speed

NFIQ2 tracks the time taken to compute each quality measure algorithm:

// Compute algorithms with timing
std::vector<std::shared_ptr<NFIQ2::QualityMeasures::Algorithm>> algorithms =
    NFIQ2::QualityMeasures::computeNativeQualityMeasureAlgorithms(rawImage);

// Get computation speeds (in milliseconds)
std::unordered_map<std::string, double> speeds =
    NFIQ2::QualityMeasures::getNativeQualityMeasureAlgorithmSpeeds(algorithms);

// Display timing information
std::cout << "Computation speeds:\n";
for (const auto &[algorithmID, durationMS] : speeds) {
    std::cout << algorithmID << ": " << durationMS << " ms\n";
}

// Calculate total processing time
double totalTime = 0.0;
for (const auto &[_, duration] : speeds) {
    totalTime += duration;
}
std::cout << "Total processing time: " << totalTime << " ms\n";

Speed measurements are useful for performance profiling and identifying bottlenecks in your image processing pipeline.

Working with Specific Measures

Accessing Individual Measures

std::unordered_map<std::string, double> qualityFeatures =
    NFIQ2::QualityMeasures::getNativeQualityMeasures(algorithms);

// Check if image has sufficient foreground
if (qualityFeatures.count("SufficientFingerprintForeground")) {
    bool hasForeground = 
        qualityFeatures.at("SufficientFingerprintForeground") > 0.5;
    if (!hasForeground) {
        std::cout << "Warning: Insufficient fingerprint foreground\n";
    }
}

// Get minutiae count
if (qualityFeatures.count("FingerJetFX_MinutiaeCount")) {
    double minutiaeCount = 
        qualityFeatures.at("FingerJetFX_MinutiaeCount");
    std::cout << "Detected " << minutiaeCount << " minutiae\n";
}

// Get orientation certainty
if (qualityFeatures.count("OCL_Bin10_Mean")) {
    double orientationCertainty = 
        qualityFeatures.at("OCL_Bin10_Mean");
    std::cout << "Orientation certainty: " << orientationCertainty << '\n';
}

Quality Measure Categories

Binary Quality Checks

These measures return 0 or 1 to indicate pass/fail:

EmptyImageOrContrastTooLow: Image has sufficient contrast
UniformImage: Image is not uniform
FingerprintImageWithMinutiae: Minutiae were detected
SufficientFingerprintForeground: Adequate foreground area

Frequency Domain (FDA)

Analyzes ridge frequency patterns:

FDA_Bin10_Mean: Mean frequency domain analysis
FDA_Bin10_StdDev: Standard deviation of FDA
FDA_Bin10_0 through FDA_Bin10_9: Frequency bins

Minutiae Features

Measures based on detected minutiae:

FingerJetFX_MinutiaeCount: Total number of minutiae
FingerJetFX_MinCount_COMMinRect200x200: Minutiae in center region
FJFXPos_Mu_MinutiaeQuality_2: Minutiae quality metrics
FJFXPos_OCL_MinutiaeQuality_80: High-quality minutiae count

Orientation Features

Ridge orientation analysis:

OCL_Bin10_Mean: Mean orientation certainty level
OCL_Bin10_StdDev: Standard deviation of OCL
OF_Bin10_Mean: Mean orientation flow
OrientationMap_ROIFilter_CoherenceRel: Orientation coherence

Ridge Quality

Ridge-valley structure analysis:

RVUP_Bin10_Mean: Ridge valley uniformity
LCS_Bin10_Mean: Local clarity score
MMB: Minutiae-based measure
Mu: Mean grayscale intensity

Actionable Quality Feedback

In addition to raw measures, NFIQ2 provides actionable feedback for improving image quality:

// Get actionable quality feedback IDs
std::vector<std::string> actionableIDs =
    NFIQ2::QualityMeasures::getActionableQualityFeedbackIDs();

// Compute actionable feedback from algorithms
std::unordered_map<std::string, double> actionableQuality =
    NFIQ2::QualityMeasures::getActionableQualityFeedback(algorithms);

// Display actionable feedback
for (const auto &actionableID : actionableIDs) {
    std::cout << actionableID << ": "
              << actionableQuality.at(actionableID) << '\n';
}

Actionable feedback can guide users to improve capture quality by identifying specific issues.

Complete Example

Here’s a complete example that extracts and displays all quality information:

#include <nfiq2.hpp>
#include <iostream>

int main() {
    // Load image (simplified)
    NFIQ2::FingerprintImageData rawImage = /* ... */;

    // Compute algorithm objects
    std::vector<std::shared_ptr<NFIQ2::QualityMeasures::Algorithm>> algorithms;
    try {
        algorithms = NFIQ2::QualityMeasures::
            computeNativeQualityMeasureAlgorithms(rawImage);
    } catch (const NFIQ2::Exception &e) {
        std::cerr << "Error: " << e.what() << '\n';
        return EXIT_FAILURE;
    }

    // Extract quality measures
    std::unordered_map<std::string, double> qualityFeatures =
        NFIQ2::QualityMeasures::getNativeQualityMeasures(algorithms);

    std::cout << "Native Quality Measures:\n";
    for (const auto &[featureID, value] : qualityFeatures) {
        std::cout << "  " << featureID << ": " << value << '\n';
    }

    // Get computation speeds
    std::unordered_map<std::string, double> speeds =
        NFIQ2::QualityMeasures::getNativeQualityMeasureAlgorithmSpeeds(algorithms);

    std::cout << "\nComputation Speeds:\n";
    for (const auto &[algorithmID, duration] : speeds) {
        std::cout << "  " << algorithmID << ": " << duration << " ms\n";
    }

    // Get actionable feedback
    std::unordered_map<std::string, double> actionableQuality =
        NFIQ2::QualityMeasures::getActionableQualityFeedback(algorithms);

    std::cout << "\nActionable Feedback:\n";
    for (const auto &[actionableID, value] : actionableQuality) {
        std::cout << "  " << actionableID << ": " << value << '\n';
    }

    return EXIT_SUCCESS;
}

Use Cases

Quality Diagnostics

Identify specific quality issues in captured fingerprints

Performance Profiling

Measure and optimize processing times

Custom Scoring

Build custom quality metrics for specific use cases

Research Analysis

Analyze individual quality components for research

Next Steps

Computing Scores

Learn how to compute unified quality scores

API Reference

Detailed API documentation for quality measures

Documentation Index

​Overview

​Computing Quality Measures

​Approach 1: Direct Computation

​Approach 2: From Algorithm Objects

​Listing Available Measures

​Measuring Computation Speed

​Working with Specific Measures

​Accessing Individual Measures

​Quality Measure Categories

​Actionable Quality Feedback

​Complete Example

​Use Cases

Quality Diagnostics

Performance Profiling

Custom Scoring

Research Analysis

​Next Steps

Computing Scores

API Reference

Overview

Computing Quality Measures

Approach 1: Direct Computation

Approach 2: From Algorithm Objects

Listing Available Measures

Measuring Computation Speed

Working with Specific Measures

Accessing Individual Measures

Quality Measure Categories

Actionable Quality Feedback

Complete Example

Use Cases

Next Steps