On Feature Extraction using Region-based Measures for Fingerprint Recognition Anitha P. C, Douglas Antony Louis Piriyakumar Department of Computer Science & Engineering, PES Institute of Technology, Banglaore-560 085, India. Email-id:
[email protected],
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Abstract Most of the Automated Fingerprint Identification Systems (AFIS) are based on the minutiae (ridge endings and bifurcations) matching. In low quality fingerprints, the automatic detection of the minutiae is a very critical process. Further minutiae based matching has difficulty in quickly matching two fingerprint images containing different number of unregistered minutiae points. Real-time applications require the results with minimum false positives and true negatives [1]. This essentially demands more features to be extracted which will minimize the error in recognition. In this paper, we have designed a feature extraction strategy to extract desired (user defined) number of features [2]. This paper describes a simple fingerprint recognition system that constitutes fingerprint preprocessing, feature extraction and matching. The proposed method uses region-based measures for feature extraction. The technique uses very less number of minutiae details and tries to gather the information from every region of the fingerprint. Finally, the extracted features of the query fingerprint are matched with the template database to recognize the fingerprint. Keywords: Pattern Recognition; Neural Networks; Biometrics; Fingerprint Recognition; Regionbased features; Minutiae Extraction; Matching
1. Introduction Lot of interests are evinced on biometric based authentication systems due to wide industrial applications [3]. Automated biometrics deal with the authenticating and identifying human beings based on their physiological or behavioural characteristics such as face, fingerprint, hand geometry, voice, hand vein, iris, retina and signature. Fingerprint identification is one of the time tested (approximately 50% above of the biometric technology share) and reliable biometric identification methods. Fingerprints are used for personal identification in the applications such as access control in restricted areas, criminal investigation including forensic science, driving license, network logon, electronic banking, airport check-in [4, 21]. In the identification mode, fingerprint recognizes an individual by searching the entire template database for a match. A fingerprint is the pattern of ridges and furrows on the surface of the finger tip [5]. The central region of the fingerprint gives very important global ridge and furrow structure that is used to classify the type of the fingerprint. There are local ridge and furrow minute details (spurs, islands, dot, snort ridge, crossover, enclosure, bridges) all over the fingerprint which uniquely identify that fingerprint [6]. Fingerprint recognition refers to the process of matching query fingerprint against a given fingerprint database to establish the identity of an individual. The retrieval speed and the accuracy are the two critical issues in the process. Several approaches are discussed in the literature to detect these features [7, 8]. In this paper, we propose a simple and fast fingerprint recognition system. Feature extraction tessellates the region of interest of the fingerprint image with core as the reference point. Core point is the point at which a maximum direction change is detected in the orientation field of a fingerprint image or the point at which directional field becomes discontinuous. Several methods have been proposed for core point detection [9, 10, 11]. A feature vector is composed of ordered enumeration of features from subregion and orientation details. In order to obtain good result, system requires a good quality image which can be obtained by fingerprint sensors or a rigorous preprocessing done on the low quality image [22]. Section 2 vividly portrays the preprocessing of the fingerprints and the feature extraction and section 3 explains the matching of the fingerprint with the available fingerprints. Experimental findings are presented in Section 4. Finally, section 5 concludes the paper.
2. Fingerprint Recognition 2.1. Preprocessing The traditional fingerprints are obtained by placing inked fingertip on paper. Now compact solid state sensors viz., scanners are used. The fingerprints are scanned using optical sensors, electrical field sensors, capacitive sensors, ultrasonic sensors, temperature sensors, pressure sensors and others. The solid state sensors are used to obtain patterns that can have image size of 480 x 508 pixels at 500 dpi. Feature extraction algorithms described in many papers [12,13] extract features based on local ridges and furrow minute details, which form special pattern in the central region of the fingerprint. Fingerprint is recognized based on features such as ridge endings and bifurcations (also known as minutiae), core, delta. These features are shown in the figure 1.
Figure 1. Minutiae, ridge endings and ridge bifurcations. A fingerprint image preprocessor receives an input fingerprint image, binarizes it and finally outputs a higher level feature based image of the fingerprint as explained below. 1. The input is a gray level fingerprint image. 2. Histogram equalization is done to determine the proper threshold value is determined. 3. Any gray value above the threshold value is considered to be white (0) and any gray value below the threshold is considered to be black (1). 4. Binarized image is created which consists of black and white pixels. 5. Noise pruning is done using a filter algorithm which checks the neighbourhood (8 pixels) and removes any isolated pixel in the neighbourhood.
2.2. Feature Extraction Feature extraction is divided into two phases as shown in figure 2. Phase 1 segments the binarized image into various subregions and extracts the respective region values. Phase 2 extracts the prominent minutiae and the corresponding orientation details of the fingerprint image.
Phase 1 1.
2.
3.
The N x N binary image is divided into several subregions as shown in figure 3, viz, Horizontal subregions - H1, H2, H3, H4 Vertical subregions - V1, V2, V3, V4 Corner regions - C1, C2, C3, C4 Central region - C A perfect square is extracted from the image and diagonal subregions are obtained as D1, D2, D3, D4. The region value (RV) for each subregion is calculated by taking the ratio of number of ON pixels (1’s) to the total number of pixels in the subregion. A set of region values is obtained for each of the fingerprint in the database which is carried out offline.
Figure 2. Flow diagram of the proposed system
Figure 3. Subregion features (RV) of a binarized fingerprint.
Phase 2 1. The noise pruned fingerprint image is thinned. 2. The core is detected as reference point (xc, yc) and its orientation is noted θcore using the technique explained in the paper [14,15]. 3. Minutiae detection is a trivial task when an ideal thinned ridge map is obtained [16]. Minutiae are extracted and the distance value with respect to the core is measured using the equation
di= sqrt((xi-xc)2+(y-yc)2)
4. The interdistance between two minutiae are also checked and a minutiae falling inside a minimum distance is ignored. So, any two minutiae’s are at a reasonable distance. 5. The orientation of each minutia is also calculated. -1 θi = tan (( yi-yc)/(xi-xc)) 6. The position of each minutia is noted and the subregion to which it belongs is determined [17]. 7. A feature vector for each fingerprint is prepared by combining the region values obtained by phase 1 and the number of minutiae that is present in each of the subregion. 8. Further to enhance the recognition accuracy, we also note the orientation of the core and with respect to delta which can also be used to classify different types of fingerprints (left loop, right loop, whorl, tented arch, twin loop) [18, 19]. The processes involved in phase 1 and phase 2 of the proposed system is shown in the figures 3 and 4. Fingerprint F1 Æ core-delta orientation=470 Subregion Region Value H1 H2 H3 H4 V1 V2 V3 V4 C1 C2 C3 C4 D1 D2 D3 D4 C
0.513324 0.503391 0.534554 0.564393 0.343391 0.575793 0.554595 0.603396 0.467392 0.552367 0.345345 0.523341 0.427523 0.433342 0.534491 0.503499 0.603291
Number of Minutiae 4 3 3 2 3 4 5 4 2 3 2 3 5 4 5 4 5
Figure 5. Feature vector generated after feature extraction. Figure 4. Orientation of core with respect to delta in different types of fingerprints.
3. Classification and Matching The features obtained in the first phase and the second phase of the feature extraction method are stored as a vector in a fingerprint database. Once the feature vector is extracted for the query fingerprint, the feature vector is matched with the vectors in the database. The searching method is very simple. First, the
orientation of the core with respect to delta is stored in the vector which is the only value that is first searched which helps in classifying the fingerprint at a faster rate[18]. Secondly, the region values and the number of minutiae present in each subregion are matched using distance measures. The features extracted from phase 1 and phases 2 are stored in a vector as shown in the figure 5.
4. Results and Analysis The first dataset contained around 200 fingerprints collected from four databases of FVC2002 [20]. Some of the fingerprints in the database are captured by capacitive sensor with resolution of 500 pixels per inch. The second dataset contained around 130 fingerprints which were captured with a scanner manufactured by Hewlett Packard. The size of the images is 380 x 380 with 450-dpi. The data acquisition process was carried out by research assistants by placing their fingers in the centre of the scanner and in the upright position. Due to their assistance, most of the fingerprints captured were reasonably well centered. All the experiments are carried out on a PC with the configuration AMD Athlon 1.5 GHz, 512 MB RAM, 80 GB hard disk. The algorithms that are used in the fingerprint recognition system are implemented in programming language C. The different modules for various functions were designed as follows. 1) Fingerprint preprocessor- Binarization, Noise pruning 2) Thinning 3) Reference point detection 4) Fingerprint segmentation 5) Region value extraction from different regions 6) Minutiae extraction 7) Orientation detection 8) Generate feature vector 9) Match the test fingerprint with the template database To evaluate the performance of the system, number of the features that were extracted was varied. The subregions were selected in random order and checked. Table I shows results for different number of subregions. Only the prominent minutiae were considered which reduced the extraction time. Results of different number of minutiae matching and the corresponding equal error rates (EER) are shown in Table II.
Table I. Timing of the various processes in phase 1 Number of subregions Time for preprocessing (s) Time for Region Value extraction (s) Time for minutiae extraction (s) Time for matching(s) Total time(s)
8
12
16
19
0.153 0.152 0.165 0.110 0.580
0.153 0.159 0.166 0.110 0.588
0.153 0.164 0.172 0.110 0.599
0.153 0.167 0.172 0.110 0.602
Table II. Equal Error Rates for different number of minutiae matching Number of matching minutiae 8 12 15
EER (%) 9.5% 7.5% 5.2%
Ratio of subregions to the full image size 0.245 0.476 0.698
Number of Pixels from the extraction subregion 75 112 165
5. Conclusion In this work, a new variable feature extraction approach is introduced for fingerprint recognition. The experimental results prove the accuracy and robustness of the new method and the comparison with other techniques demonstrates its superiority for the continuous classification task. The proposed system is very simple and flexible. The user can choose any number of subregions. Hence the feature values can be varied
depending on the allowable acceptance rate of the application. This system does not involve very critical minutiae detection which is very crucial in other systems. The orientation of the image is critical for recognition. The results of the system on low quality fingerprint images have to be improved which is continued as extension. The important aspect in the system is the response obtained in very short time. The system works well for smaller organizations where the fingerprint capturing sensor gives good quality image and where people are trained to press the finger in a particular orientation. Future work will be dedicated to the definition and experimentation of a new set of prototypes that help in still better feature extraction and the study and analysis between different feature extraction algorithms.
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