A Review of
“Adaptive fingerprint image
enhancement with fingerprint
image quality analysis”,
by Yun & Cho
Malcolm McMillan
From “Image & Vision Computing”, volume 24.
All images taken from article except where quoted.
Talk Structure
Background concerning fingerprint identification.
Categorizing fingerprints.
Adaptive enhancement method.
Why is fingerprint identification important?
Due to uniqueness of a person’s fingerprint, fingerprint
analysis plays an important role in identification processes
such as:
 Crime scene investigations
The Fingerprint
Believed to be unique to each person.
Consists of “ridges” and
Ridges = single, curved
segments, black lines.
Valleys = area in between
ridges, white.
The Identification Process
Quality Issues
Success of fingerprint identification heavily dependent
upon quality of fingerprint image.
Fingerprints are often poor quality due to environmental
and skin condition factors.
Thus enhancement processes are key to successful
Features used for identification
Identification carried out by
extracting location of
features, known as “minutiae”.
2 types of feature:
- Ridge Endings
- Ridge Bifurcations
Quality Issues
Poor quality images lead to:
a) genuine minutiae being
b) spurious minutiae
being identified
eg a broken ridge will
have multiple false ridge
The Identification Process
The Identification Process
Aim: To enhance key
features of the image in
order to allow minutiae to
be more successfully
Traditional techniques have applied a uniform
enhancement to all fingerprints,
ie the same method has been used regardless of the state
of the original fingerprint.
The aim of this paper is to develop an adaptive
enhancement technique,
ie one that takes into account the state of the original
image and selects an enhancement technique appropriate
to this.
Fingerprint Categories
Fingerprints divided into 3 categories
1. Neutral Image.
Image as normal.
Fingerprint Categories
Fingerprints divided into 3 categories
Oily Image.
Image generally darker due to some parts of valleys being filled
up (thus appearing black rather than white).
Ridges either very thick or, in the extreme, merged into one.
Fingerprint Categories
Fingerprints divided into 3 categories
3. Dry Image.
Image generally lighter.
Ridge lines broken (due to gaps of white along ridge).
Ridges lines thin.
Enhancement Overview
So adaptive enhancement recognises that a single enhancement
process is not going to be optimal for all categories. Instead we
want to enhance different categories in different ways:
Oily Image:
Valley enhancement – dilate/connect thin/disconnected valleys.
Neutral Image:
No enhancement required.
Dry Image:
Ridge enhancement - dilate/connect thin/disconnected ridges.
Selection Criteria
Now we need to define the criteria we will use to assign each
fingerprint to a particular category.
5 Criteria Used:
1. Mean
2. Variance
3. Block Directional Difference
4. Orientation Change
5. Ridge - valley thickness ratio
A Clustering Algorithm using these criteria then assigns
fingerprints to the appropriate class.
Adaptive Enhancement
Now that we have assigned fingerprints to their class we are in
a position to perform a different enhancement process on each
Enhancement of Dry Images
Want to “join up” ridges so false
minutiae not detected.
Extract centre lines of ridges and
remove white pixels in ridge (ie
connect ridges) using the centrelined image.
Ridge Enhancement Process
Reduces noise.
3. Dilating
White ridge pixels
Reduces image to basic
structure of ridges.
4. Union of dilated and original
image taken to give original
image with broken ridges
“joined up”.
Experimental Results
Now we have the theory behind the process of Adaptive
Enhancement, we must apply it to a set of data to see if it
actually improves fingerprint identification.
Analyzed 2000 fingerprints according to 5 criteria outlined
previously and used clustering to assign fingerprints to one of
dry, oily, or normal.
Experimental Results: Clustering
Experimental Results: Enhancement
Now we have categorized our fingerprints we can perform
adaptive enhancement and compare our results with
conventional enhancement.
Adaptive filtering yields
improved results over
conventional methods.
Left-hand side = conventionally enhanced.
Right-hand side = adaptively enhanced.
Feature Extraction
To the eye, we can see that adaptive enhancement produces a
better image.
This is borne out when we
extract features from both
conventionally enhanced
and adaptively enhanced
Images (a) & (c) enhanced conventionally.
Images (b) & (d) enhanced adaptively.
Conclusions 1
Fingerprint identification relies on image quality.
Adaptive enhancement shows an improvement in image
quality over conventional enhancement.
Measuring Quality Quantitatively
Quality measured quantitatively calculating proportion
of correctly identified minutiae.
The experimental results indicate an improvement
from 92% to 96% in correctly identified fingerprints.
Conclusions 2
Estimated increase in computational time for adaptive
enhancement is approximately 0.5 seconds per fingerprint.
Is the increase in quality worth the wait?
Further Issues
• Is 3 the optimum number of classes?
• Can we develop other enhancement schemes for classes
of fingerprints with different properties?
• If so, can we get better results with more classes?