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Fingerprint Recognition System : Design & Analysis

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Fingerprint Recognition is one of the research hotspots in Biometrics. It refers to the automated method of verifying a match between two human fingerprints. It is essentially a challenging pattern recognition problem where two competing error rates: the False Accept Rate (FAR) and the False Reject Rate (FRR) need to be minimized. Advancement of computing capabilities led to the development of Automated Fingerprint Authentication Systems (AFIS) and this led to extensive research especially in the last two decades. In this paper, we attempt to give a comprehensive scoping of the fingerprint recognition problem and address its major design and implementation issues as well as give an insight into its future prospects.
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Fingerprint Recognition System : Design & Analysis
Dibyendu Nath
Saurav Ray
Sumit Kumar Ghosh
Dept. of Computer Science & Engineering,
Heritage Institute of Technology,
Kolkata, India.
Abstract Fingerprint Recognition is one of the research
hotspots in Biometrics. It refers to the automated method of
verifying a match between two human fingerprints. It is
essentially a challenging pattern recognition problem where two
competing error rates: the False Accept Rate (FAR) and the
False Reject Rate (FRR) need to be minimized. Advancement of
computing capabilities led to the development of Automated
Fingerprint Authentication Systems (AFIS) and this led to
extensive research especially in the last two decades. In this
paper, we attempt to give a comprehensive scoping of the
fingerprint recognition problem and address its major design
and implementation issues as well as give an insight into its
future prospects.
Keywords- Fingerprint Recognition, Biometrics, Identification,
Verification, Security, Authentication
In order to access the Internet or any other important
resource safely, high-security authentication systems are
essential. However studies [10] show that users usually
choose weak passwords, frequently re-use passwords across
multiple sites and often forget them. According to the 2002
NTA Monitor Password Survey, heavy web users have an
average of 21 pass-words, 81% of users choose a common
password and 30% write their passwords down or store them
in a file. Automated identity authentication using fingerprint
recognition [4, 3] is an effective solution in such cases.
Historically speaking, fingerprints have been long
associated with criminology, specifically forensics.
Development of cheaper and robust automated fingerprint
authentication systems coupled with the inherent ease of
fingerprint acquisition, has led to its widespread commercial
and civilian applications. One of the world’s largest
fingerprint recognition systems is the Integrated Automated
Fingerprint Identification System (IAFIS), maintained by the
FBI in the US since 1999.
A. Fingerprint as a Biometric
“Two like fingerprints would be found only once every 10
years” Scientific American, 1911.
Individuality of fingerprints is based on empirical
observations. However Golfarelli et al [6] formulated the
optimum Bayesian decision criterion for a biometric
verification system and obtained a theoretical equal error rate
(EER) of 1.31 x 10
for a hand-geometry-based verification
system and of 2 x 10
for a face-based verification system.
Similarly Pankanti et al [5] also showed that there is limited
probability of correspondence of two fingerprints.
B. Classification & Indexing of Fingerprints
Fingerprint authentication includes two subdomains: one
is fingerprint verification (Am I who I claim I am?) and the
other is fingerprint identification (Who am I?), the latter
being more difficult requiring extensive indexing and
classification of fingerprints for efficient retrieval.
a. Arch b. Left Loop c. Right Loop
d. Tented Arch e. Twin Loop f. Whorl
Fig. 1: Fingerprint classification involving 6 classes - critical points in a
fingerprint called core & delta marked as circles & triangles
Nearly all fingerprint classification schemes used today
are derived from the famous ―Henry System‖ [1] a detailed
fingerprint indexing method for aiding manual fingerprint
comparison. For instance, the FBI uses one variant which
recognizes eight different types of patterns: radial loop, ulnar
loop, double loop, central pocket loop, plain arch, tented
arch, plain whorl, and accidental.
Whorls are usually circular or spiral in shape. Arches
have a mound-like contour, while tented arches have a spike-
like or steeple-like appearance in the center. Loops have
concentric hairpin or staple-shaped ridges and are described
as "radial" or "ulnar" to denote their slopes; ulnar loops slope
toward the little finger side of the hand, radial loops toward
the thumb.
Fingerprint classification & indexing is a difficult pattern
recognition problem due to small inter-class variability
compared to large intra-class variations in fingerprint
patterns. Germain et al [15] describe a popular efficient
technique for indexing into large fingerprint databases using
minutiae triplets in their indexing procedure. More efficient
classification schemes have also been proposed like [7] by
Jain, et al.
A fingerprint is an impression of the epidermal ridges of a
human fingertip. A hierarchy of three levels of features,
namely, Level 1 (pattern), Level 2 (minutiae points) and
Level 3 (pores and ridge shape) are used for recognition
purposes. Most AFISs employ Level 1 & Level 2 features.
Level 1 features refer to the overall pattern shape of the
unknown fingerprinta whorl, loop or some other pattern.
This level of detail cannot be used to individualize, but it can
help narrow down the search. Level 2 features refers to
specific friction ridge paths overall flow of the friction
ridges and major ridge path deviations (ridge characteristics
called minutiae) like ridge endings, lakes, islands,
bifurcations, scars, incipient ridges, and flexion creases.
a. Level 1 b. Level 2
c. Level 3
Fig. 2: Fingerprint Features
Level 3 detail [14] refers to the intrinsic detail present in a
developed fingerprint pores, ridge units, edge detail, scars
etc. High resolution sensors (1000dpi) are required for
extraction of Level 3 features. But as [8] shows, EER values
are reduced (relatively 20%) using them along with Level 1
& 2 features. Moreover Level 3 features offer greater success
in partial fingerprint recognition as shown in [9].
Fingerprint sensing techniques can be of two types off-
line scanning and live-scanning. In off-line sensing
fingerprints are obtained on paper by “ink technique” which
are then scanned using paper scanners to produce the digital
image. Most AFISs use live-scanning where the prints are
directly obtained using an electronic fingerprint scanner.
Almost all the existing sensors belong to one of the three
families: optical, solid-state, and ultrasound.
Optical sensors, based on the frustrated total internal
reflection (FTIR) technique are commonly used to capture
live-scan fingerprints in forensic and government
applications. They are the most common fingerprint sensors.
An important breakthrough in sensor technology was the
development of optical sensors based on fiber-optics as
described in the US patent [21], leading to sensor
miniaturization and enhanced portability.
a.Capacitive Solid-State Sensor [20]
b. Optical Sensor using FTIR
Fig. 3: Fingerprint Sensors
Solid-state touch and sweep sensors silicon-based
devices that measure the differences in physical properties
such as capacitance or conductance of the friction ridges and
valleys dominate in commercial applications. Tartagni and
Guerrieri [22] describe a feedback capacitive sensing scheme
using a 200x200 element sensor array implement in standard
2-metal CMOS technology. Jeong-Woo Lee et al [20]
discusses another such solid-state sensor, based on capacitive
differences, capable of producing 600dpi fingerprints. Many
commercially available sweep sensors like Fujitsu MBF320
are based on such low-power solid-state devices.
Fig. 4: Latent Fingerprint
been made in plain fingerprint matching, latent fingerprint
matching continues to be a difficult problem. Poor quality of
ridge impressions, small finger area, and large non-linear
distortion are the main difficulties in latent fingerprint
matching, compared to plain fingerprint matching.
A special case of off-line
sensing is the acquisition of a
latent fingerprint from a crime
scene [19]. Used extensively in
forensics, latent prints are
accidental impressions left by
friction ridge skin on a surface,
due to natural secretions of the
eccrine glands present on skin.
While tremendous progress has
d. Fingerprint Minutiae
For the purpose of automation, a suitable representation
i.e. feature extraction of fingerprints is essential. This
representation should have the following properties
Retention of discriminating power of each
fingerprint at several levels of resolution
Easy computability
Amenable to automated matching algorithms
Stable and invariant to noise and distortions
Efficient and compact representation
Several feature extraction methods have been proposed
and implemented successfully over the years. Roughly
speaking there are four categories of methods based on
fingerprint feature extraction by image processing [11].The
first category of methods extract minutiae directly from the
gray-level image [1, 23, 25, 34] without using binarization
and thinning processes while the second category extracts
features from binary image profile patterns [15, 25, 26]. The
third category of methods uses machine learning [25, 28, 29]
for extracting minutiae and the last category extracts
minutiae from binary skeletons [2, 30].
a. Original Gray-scale Image b. Binarized Image
c. Orientation Field d. Image after Ridge Thinning
Fig. 4: Minutiae Extraction from Fingerprint Image
Binarization is the process by which an enhanced gray-
level image is transformed into a binary image for subsequent
feature detection. Good binarization algorithms should
minimize information loss and also provide efficient
computational complexity. A binarization approach based on
the peak detection in the cross section gray-level profiles
orthogonal to the local ridge orientation has been proposed
by Ratha, et al [31]. Liang et al [27] proposed an Euclidean
distance transform method to obtain a near-linear time
binarization of fingerprint images.
Fingerprint ridge thinning is basically elimination of
redundant pixels till each ridge is just one pixel thick. An
innovative iterative thinning technique has been proposed by
Ahmed and Ward [32] while a multi-scale thinning approach
has been proposed by You, et al [33].
a.Minutiae after marking b. Real Minutiae after false removal
Fig. 5: Minutiae Extraction
After initial fingerprint feature extraction some post-
processing is required for removing false or spurious
minutiae detected in highly corrupted regions or introduced
by previous processing steps (e.g., thinning). Chen and Kuo
[24] proposed a three-step false minutiae filtering method,
which dropped minutiae with short ridges, minutiae in noise
regions, and minutiae in ridge breaks using ridge direction
information. Another method for removing all the spurious
pixels generated at the thinning stage in order to facilitate
subsequent minutiae filtering has been proposed by Zhao and
Tang [30].
Matching fingerprint images is an extremely difficult
problem, mainly due to the large variability in different
impressions of the same finger (i.e., large intra-class
variations). Fingerprint matching algorithms are roughly
classified into 3 major categories
C. Correlation-based Matching :
Two fingerprint images are superimposed and the
correlation between corresponding pixels is computed for
different alignments (e.g. various displacements and
rotations). Fourier transform [12] as well as Fourier-Mellin
Transform [13] can be used to speed up the correlation
D. Feature-based (or Minutiae- based) Matching :
Typical fingerprint recognition methods employ feature-
based matching, where minutiae (i.e., ridge ending and ridge
bifurcation) are extracted from the registered fingerprint
image and the input fingerprint image, and the number of
corresponding minutiae pairings between the two images is
used to recognize a valid fingerprint image. Alternatively,
Jain et al. [2] used a string matching technique while Isenor
and Zaky [17] propose a graph-based fingerprint matching
algorithm. Fan et al. [18] describes a fingerprint verification
algorithm based on a bipartite graph construction between
model and query fingerprint feature clusters.
The minutiae matching problem has been generally
addressed as a point pattern matching problem which has
been extensively studied yielding families of approaches
known as relaxation methods, algebraic and operational
research solutions, tree-pruning approaches, energy-
minimization methods, Hough transform, etc.
E. Pattern-based (or Image-based) Matching
Pattern based algorithms compare the basic fingerprint
patterns (e.g., local orientation and frequency, ridge shape,
texture information) between a previously stored template
and a candidate fingerprint. The images need to be aligned in
the same position, about a central point on each image. The
candidate fingerprint image is then graphically compared
with the template to determine the degree of match.
The image-based techniques include both optical as well
as computer-based image correlation techniques. Recently,
several transform-based techniques have also been explored.
For instance, a phase-based fingerprint image matching
technique using 2D discrete Fourier transforms has been
proposed by Ito, et al [35] while Hamamoto [16] describes a
Gabor filter based fingerprint matching technique.
A fingerprint recognition system can make two types of
errors: a false match, when a match occurs between images
from two different fingers, and a false non-match, when
images from the same finger are not a match. Thus the chief
objective behind the design of a good fingerprint matching
system is to reduce both these errors. However both the error
rates cannot be reduced simultaneously as they are inversely
dependent on each other.
Another important design issue is the security of the
fingerprint recognition system itself along with the
fingerprint template database. The unauthorized use or
disclosure of fingerprint template information from such
databases can be a serious security and privacy threat.
Although fingerprint recognition has been extensively
studied, there are still many open research problems in this
domain, for instance :
Efficient Automated Fingerprint Classification
Fully Automated Latent Fingerprint Recognition
Altered or Fake Fingerprint Detection
Efficient Compression of Fingerprint Templates
Automated Artificial Fingerprint Generation
Latent fingerprint matching poses another whole new set
of problems altogether. Compared to good quality full
fingerprints acquired using live-scan or inking methods
during enrollment, latent fingerprints are often smudgy and
blurred, capture only a small finger area, and have large
nonlinear distortion. Hence they require enhanced extraction
and matching techniques to make latent fingerprint
recognition free of manual matching and fully automated.
Fingerprint Authentication has been studied for well over
a century. However, its use has truly become widespread and
mainstream only in the last few decades due to development
of automated fingerprint recognition systems. The ever-
increasing demand for reducing the error and failure rates of
automated fingerprint recognition systems and the need for
enhancing their security have opened many interesting and
unique research opportunities that encompass multiple
domains such as image processing, computer vision,
statistical modeling, cryptography, and sensor development.
Our preliminary analysis shows that fingerprints have been
proven to be an excellent if not the best biometric and its
potential has not yet been fully realized.
But still, issues such as fingerprint authentication at a
distance, real-time identification in large-scale applications
with billions of fingerprint records, developing secure and
revocable fingerprint templates that preserve accuracy, and
scientifically establishing the uniqueness of fingerprints will
likely remain as grand challenges in the near future.
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... Various methods are proposed for this minutiae based fingerprint recognition [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] . Research also takes on nonminutiae based fingerprint features [36, 37]. ...
Biometric technology is used to identify a person based on his/her physical behavioral characteristics. One of the extensive uses of biometric technology is a fingerprint recognition system. The technology has broad use mainly for its easiness, reliability and accuracy in human identification process. This paper presents work done on minutiae based palmtop recognition system for automatic door open and locking system. Here, the palmtop recognition system works by taking an image of the person, partitioning it, processing it and finally verifying the person. This system provides input for an electric circuit. The circuitry system consists of two unique states; door open and door lock. The whole system basically uses extensive Image processing for minutiae based palmtop recognition. Thus reducing the probability of error in human recognition and solves maximum problems of fingerprint recognition. This paper shows a better solution for recognizing people, which helps to solve security related problems in human life.
Fingerprint analysis is the most essential part of human identification or human recognition. At present too many biometric techniques are presented for fingerprint identification and fingerprint recognition. We know that, a fingerprint contains a lot of key point like Y shape, delta, ridge ending, ridge staring, minutiae’s pattern and etc. All points are apodictic of unique for any human fingerprint. The aim of this paper is to review numerous recently work on fingerprint recognition system. Fingerprint detection is a very important topic to identify the correct person’s finger print and can make everything secure. The main idea of this research paper is to find out the 100% correct fingerprint details from any document (in image format). In this research paper we will get that how to identify the details of fingerprint from image.
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Matching and Classification: A Case Study in Fingerprint Domain
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The performance of any fingerprint recognizer highly depends on the fingerprint image quality. Different types of noises in the fingerprint images pose greater dif-ficulty for recognizers. Most Automatic Fingerprint Identification Systems (AFIS) use some form of image enhancement. Although several methods have been de-scribed in the literature, there is still scope for improvement. In particular, effective methodology of cleaning the valleys between the ridge contours are lacking. We observe that noisy valley pixels and the pixels in the interrupted ridge flow gap are "impulse noises". Therefore, this paper describes a new approach to fingerprint image enhancement, which is based on integration of Anisotropic Filter and direc-tional median filter(DMF). Gaussian-distributed noises are reduced effectively by Anisotropic Filter, "impulse noises" are reduced efficiently by DMF. Usually, tra-ditional median filter is the most effective method to remove pepper-and-salt noise and other small artifacts, the proposed DMF can not only finish its original tasks, it can also join broken fingerprint ridges, fill out the holes of fingerprint images, smooth irregular ridges as well as remove some annoying small artifacts between ridges. The enhancement algorithm has been implemented and tested on fingerprint images from FVC2002. Images of varying quality have been used to evaluate the performance of our approach. We have compared our method with other methods described in the literature in terms of matched minutiae, missed minutiae, spurious minutiae, and flipped minutiae(between end points and bifurcation points). Exper-imental results show our method to be superior to those described in the literature.
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In this paper, using the duality property of fingerprint image we develop several post-processing techniques to efficiently remove spurious minutiae. Especially, we define an H-point structure to remove several types of spurious minutiae including bridge, triangle, ladder, and wrinkle all together. Experimental results clearly demonstrate the effectiveness of the new algorithms.
Fingerprint friction ridge details are generally described in a hierarchical order at three levels, namely, Level 1 (pat- tern), Level 2 (minutiae points) and Level 3 (pores and ridge shape). Although high resolution sensors (∼1000dpi) have become commercially available and have made it possible to reliably extract Level 3 features, most Automated Fin- gerprint Identification Systems (AFIS) employ only Level 1 and Level 2 features. As a result, increasing the scan resolution does not provide any matching performance im- provement (1). We develop a matcher that utilizes Level 3 features, including pores and ridge contours, for 1000dpi fingerprint matching. Level 3 features are automatically ex- tracted using wavelet transform and Gabor filters and are locally matched using the ICP algorithm. Our experiments on a median-sized database show that Level 3 features carry significant discriminatory information. EER values are re- duced (relatively ∼20%) when Level 3 features are em- ployed in combination with Level 1 and 2 features.
This article describes the development and the forensic uses of automated fingerprint identification systems (AFIS). The AFIS technology was initially developed in order to overcome the limitations of the paper-based fingerprint collections, by digitizing the 10-print cards in computerized databases. Then technologies to automate the fingerprint feature extraction and comparison were developed, and AFIS systems were implemented on a large scale in order to improve the process of identification of repetitive offenders based on the 10-print cards. Further development of the AFIS technology allowed for the inclusion of palmprint reference databases and for the processing of fingermarks and palm marks with, as a result, the partial automation of the forensic investigation and intelligence processes. In the field of AFIS, the challenges for the future call for further automation of the feature extraction from low-quality fingerprint and fingermark images, the improvement of the interoperability of the systems on a global level, and the combination of databases, AFIS technology and scientific methodology in order to further improve the forensic friction ridge evaluation process.