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 “valleys” Ridges = single, curved segments, black lines. Valleys = area in between ridges, white. http://perso.wanadoo.fr/fingerchip/biometrics/types/fingerprint.htm 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 identification. Features used for identification Identification carried out by extracting location of features, known as “minutiae”. 2 types of feature: - Ridge Endings - Ridge Bifurcations (branches). Quality Issues Poor quality images lead to: a) genuine minutiae being ignored. b) spurious minutiae being identified eg a broken ridge will have multiple false ridge endings. The Identification Process The Identification Process Enhancement Aim: To enhance key features of the image in order to allow minutiae to be more successfully identified. Enhancement 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 2. 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 class. Enhancement of Dry Images Want to “join up” ridges so false minutiae not detected. Method: Extract centre lines of ridges and remove white pixels in ridge (ie connect ridges) using the centrelined image. Ridge Enhancement Process 1. Smoothing Reduces noise. 3. Dilating White ridge pixels eliminated. 2. Skeletonizing 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 Oily Neutral Dry 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. 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 Costs 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?