1. Improving the Performance of Fingerprint Classification
U. Rajanna, A. Erol, G.
Computer Vision Lab, University of Nevada at Reno, NV, USA
Abstract
In this work we introduce a scheme for the classification of fingerprints into one of its five possible Henry classes. It draws its ideas from a combination of new and old feature extraction techniques which include Minutiae Maps, Orientation Maps and Orientation Continuity. We provide a comparative analysis between these different feature extraction methods. To be able to examine their performances individually we need a common platform. Hence we use the K-nearest neighbor classifier for all methods. We report classification accuracies of fingerprints based on these methodologies individually. As a by-product of our comparison results we introduce a fusion strategy to improve classification accuracies. We show that fusion of two different methodologies results in classification accuracies better than Gabor Filters which is one of the well known and effective techniques for fingerprint classification. At the same time our feature extraction methodologies are relatively very efficient with regard to computation time in comparison with Gabor Filters. We also show that Gabor Filter based feature extraction is a less accurate feature descriptor compared to the traditional, Orientation Map based approach. Finally we conclude by providing a quantitative and qualitative analysis of results and show that our approach performs better in terms of performance and efficiency compared to Gabor Filters.
Orientation Continuity
Fusion Approach
Table 1 below provides a comparison for efficiency of feature extraction between the proposed methods and the Gabor Filter approach. The start time and end time for the algorithm was noted giving us a very simple time measure, Processing Time=(End time-Start time) called P. Given N images that were used Average Processing Time (AP) = P/N is also easily determined.
Fusion approach is useful in instances when errors produced by each method M1 and M2 (say Minutiae Maps and Orientation Maps) are uncorrelated. Hence we combine the results of each method to improve accuracies. A unique vote combination scheme using K-Nearest Neighbors is employed as shown in figure. K-Nearest Neighbors from each method is sorted and pooled into a set of 2k elements. These are sorted again and votes are taken to obtain classification accuracies.
The property of being continuous is akin to examining if two orientation angles lie on the same line (ridge). Orientation Continuity is defined as the process of finding how continuous an orientation angle in a given cell is with respect to orientation angles in surrounding cells. Feature vectors are constructed by labeling the Ctarget cells depending on their location with respect to Csource cell. The mean of labels within regions of spatial tessellation forms the final feature vector.
KEY: MM->Minutiae Maps, OC->Orientation Continuity, OO->Orientation Maps
Method
Processing time (approximates) for feature vector extraction (P)
No. of images used from NIST-4 (N)
Average Processing Time (AP=P/N)
Gabor Filter
360 minutes
3855
5.6 seconds OO
2 minutes
3930
0.03 seconds MM
20 minutes
3929
0.30 seconds OC
150 minutes
2.29 seconds
Table 1
Problem Statement
Provide a comparative study between different feature extraction methods, namely
Minutiae Maps
Orientation Continuity
Orientation Maps
Gabor Filters
As a By-product of comparative study we introduce “Fusion” strategy
Fuse (Minutiae Maps + Orientation Maps)
Fuse (Orientation Continuity + Orientation Maps)
Fusion strategy improves classification accuracies and efficiency
Orientation Maps
Experiments & Results
Conclusions and Future Work
We have provided a comparative study between different feature extraction techniques for fingerprint classification and report results on accuracies of classification. To be able to compare our different feature extraction techniques on a common ground we use a K-Nearest neighbor classifier. We present a new scheme for feature extraction based on Minutiae Maps and Orientation Continuity and compare it against the traditional Orientation Map based and Gabor Filter based feature extraction methods. As a by-product of our comparisons we introduce a fusion strategy and demonstrate not only improved accuracies but also inexpensive strategies with regard to processing time required for feature extraction. We have proposed four very simple schemes for fingerprint classification and show that fusion of two different methodologies results in improved classification accuracies while also preserving efficiency. We also show that in spite of our core point extraction algorithm not being very robust our accuracies are much better than Gabor Filters. The processing time required for our feature extraction is less than half of what is required for Gabor Filters. Our classification accuracies in the fusion approach exceed those reported for Gabor Filters by almost 2% in both top class and top 2 class evaluations.
These results become more significant when we use a Multi-stage classifier which would form part of future work. Improving the robustness of our core point extraction is also something that has to be addressed in the future.
Different methods are used to construct our feature vectors. We use a K-Nearest Neighbor classifier to examine the results of classification accuracies. The NIST-4 database with an even distribution of five Henry classes is used to examine the robustness, accuracy and efficiency of our algorithms. Figure (3) and (4) show results of Top class and Top 2 class accuracies for Fusion approach using K-Nearest Neighbor classifier.
Key: OO->Orientation Maps, MM->Minutiae Maps, OC->Orientation Continuity, +->indicates fusion
Orientation maps were extracted using the PCASYS algorithm. As shown in figure the gradient information or orientation field is calculated across 32*32 square cells where each cell is of width equal to 16 pixels. The white dot in the figure denotes the core point. We perform a very simple feature extraction by constructing a vector of orientations for 14*14 cells around the core point.
Gabor Filters
(Benchmark)
Minutiae Maps
Gabor Filters are applied on the fingerprint image in four different orientations {00, 450, 900, 1350}. As shown in figure the first step is to find a registration point or core point and the image is then normalized to a constant mean and variance to reduce noise. Spatial tessellation is performed and Gabor Filters in four different directions are applied to obtain new grayscale values of pixels. The standard deviation of grayscale values within each sector defines the feature vector.
Fig Fig. 4
Figure. 5 shows results of Top class and Top 2 class accuracies for the Gabor Filter approach which forms the benchmark method against which all of our results are compared.
References
[1] A. Jain, S. Prabhakar and L. Hong, “A Multichannel Approach to Fingerprint Classification”, IEEE Trans. Pattern Analysis and Machine Intelligence, 1999
[2] A. Ross, J. Shah and A.K. Jain, “Towards Reconstructing Fingerprints From Minutiae Points”, Proc. Of SPIE Conference on Biometric Technology for Human Identification II (Orlando, USA), 2005.
[3] D. Ruta and B. Gabrys, “An Overview of Classifier Fusion Methods”, Computing and Information Systems, 2000.
Fig Fig. 2
Region ordering and spatial tessellation is performed around a core point as shown in Fig. 1. Minutiae in fingerprints represented by dots as shown in Fig. 2. Feature vectors are constructed by using Minutiae information such as Location, Distribution and Orientation.
Fig. 5