A fingerprint is classified based on only the first type of features and uniquely identified based on the second type of features (one such feature is the ridge endings and bifurcations, also known as minutiae).

Several approaches have been developed for automatic fingerprint classification. These approaches can be broadly categorized into five categories:

1) model-based. The model-based fingerprint classification technique uses the locations of singular points (core and delta) to classify a fingerprint into the five above-mentioned classes. A model-based approach tries to capture the knowledge of a human expert by deriving rules for each category by hand-constructing the models and therefore, does not require training. Accuracies of 85 percent and 87.5 percent have been reported on the NIST-4 database using these approaches;

2) structure-based. A structure-based approach uses the estimated orientation field in a fingerprint image to classify the fingerprint into one of the five classes. An accuracy of 90.2 percent with 10 percent rejection is reported on NIST-4. The neural network was trained on images from 2,000 fingers (one image per finger) and then tested on an independent set of 2,000 images taken from the same fingers. The error reported is, thus, optimistically biased. A later version of this algorithm was tested on the NIST-14 database, which is a naturally distributed database, resulting in a better performance. However, this performance improvement should be expected since the NIST-14 database contains only a small percentage of arch-type fingerprints, which pose the most difficulty for fingerprint classifiers and the neural network used in the algorithm implicitly takes advantage of this information. A similar structure-based approach, which uses Hidden Markov Models for classification, depends on a reliable estimation of ridge locations, which is difficult in noisy images. In another structure-based approach, B-spline curves are used to represent and classify fingerprints;

3) frequency-based. Frequency-based approaches use the frequency spectrum of the fingerprints for classification;

4) syntactic. A syntactic approach uses a formal grammar to represent and classify fingerprints;

5) Hybrid. Hybrid approaches combine two or more approaches for classification. These approaches show some promise but have not been tested on large databases. For example, Chong et al. report results on 89 fingerprints, Fitz and Green on 40 fingerprints, and Kawagoe and Tojo on 94 fingerprints.

We use a novel multichannel filter-based approach to make fingerprint classification.

Automatic classification of fingerprints is a difficult problem because of the small interclass variability and large intraclass variability among the five classes under consideration.

K-Nearest Neighbor Classifier

The K-nearest neighbor decision rule first finds the K nearest neighbors of the test pattern in the feature space and, then, assigns the test pattern to the class which is most frequently represented among the K nearest neighbors. The top two categories can be retrieved from the K-NN classifier corresponding to the classes which have the highest and the second highest count among the K nearest neighbors, i.e., the first recall and the second recall. The K-nearest neighbor classifier results in an accuracy of 85.4 percent for the five-class classification task when 10 nearest neighbors (K = 10) are considered. Classification accuracy does not always increase with increasing K; there exists an optimal value of K for finite training sample size classification problems.

Neural Network Classifier

We trained a multilayer feed-forward neural network using a quick propagation training algorithm. The neural network has one hidden layer with 20 neurons, 192 input neurons, and five output neurons corresponding to the five classes.

Two-Stage Classifier

The objective here is to perform a “simple” classification task using a K-NN classifier and then use a bank of two-class neural network classifiers to handle more subtle discriminations. The first stage uses the K-nearest neighbor (K = 10) classifier to yield the two most probable classes. We observed that 85.4 percent of the time, the class with the maximum frequency among the K nearest neighbors is the correct class and 12.6 percent of the time, the class with the second highest frequency is the correct class. In other words, the K-nearest neighbor classifier yields the top two classes with an accuracy of 98 percent. This result itself can be used to accurately classify fingerprints into two out of the five classes. Each fingerprint will have an entry in two of the five partitions of the database and the matching is required to be performed only in the corresponding two partitions of the database. The second stage uses 10 different neural networks for 10 different pairwise classifications. These neural networks have 192 input neurons, 20-40 hidden neurons in one hidden layer, and 2 output neurons. Each neural network is trained using the patterns from only the two corresponding classes in the training set. For example, the neural network which distinguishes between R and W is trained using only the patterns labeled R and W in the training set.

The uniqueness of a fingerprint is exclusively determined by the local ridge characteristics and their relationships. A total of 150 different local ridge characteristics (islands, short ridges, enclosure, etc.) have been identified. These local ridge characteristics are not evenly distributed. Most of them depend heavily on the impression conditions and quality of fingerprints and are rarely observed in fingerprints. The two most prominent local ridge characteristics, called minutiae, are:

1. ridge ending is defined as the point where a ridge ends abruptly;
2. ridge bifurcation is defined as the point where a ridge forks or diverges into branch ridges.

A good quality fingerprint typically contains about 40-100 minutiae.

Automatic fingerprint matching depends on the comparison of these local ridge characteristics and their relationships to make a personal identification. A critical step in fingerprint matching is to automatically and reliably extract minutiae from the input fingerprint images, which is a difficult task. However, the performance of a minutiae extraction algorithm relies heavily on the quality of the input fingerprint images. In an ideal fingerprint image, ridges and valleys alternate and flow in a locally constant direction and minutiae are anomalies of ridges, i.e., ridge endings and ridge bifurcations. In such situations, the ridges can be easily detected and minutiae can be precisely located from the thinned ridges. In order to ensure that the performance of an automatic fingerprint identification/verification system will be robust with respect to the quality of input fingerprint images, it is essential to incorporate a fingerprint enhancement algorithm in the minutiae extraction module. We use a fast fingerprint enhancement algorithm, which can adaptively improve the clarity of ridge and valley structures of input fingerprint images based on the estimated local ridge orientation and frequency.

Fingerprint matching techniques can be placed into two categories: minutae-based and correlation based.

Minutiae-based techniques first find minutiae points and then map their relative placement on the finger. However, there are some difficulties when using this approach. It is difficult to extract the minutiae points accurately when the fingerprint is of low quality. Also this method does not take into account the global pattern of ridges and furrows;

The correlation-based method is able to overcome some of the difficulties of the minutiae-based approach. However, it has some of its own shortcomings. Correlation-based techniques require the precise location of a registration point and are affected by image translation and rotation.

Fingerprint matching based on minutiae has problems in matching different sized (unregistered) minutiae patterns. Local ridge structures can not be completely characterized by minutiae. We are trying an alternate representation of fingerprints which will capture more local information and yield a fixed length code for the fingerprint. The matching will then hopefully become a relatively simple task of calculating the Euclidean distance will between the two codes.