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IJRREST
INTERNATIONAL JOURNAL OF RESEARCH REVIEW IN
ENGINEERING SCIENCE & TECHNOLOGY
(ISSN 2278–6643)
VOLUME-4, ISSUE-1, APRIL-2015
(Proceedings of “6th National Conference on Make in India PMs Vision : Role of Engineering and Management
Innovations to Achieve this Vision” Organised by Apex Group of Institutions, Karnal, Haryana, India, April-2015)
A COMPREHENSIVE SURVEY OF FINGER PRINT
RECOGNITION TECHNIQUES
1
Medhavi Sarwal, 2Shikha Khanna
ECE Department, DVIET, Karnal, Haryana, India
Email: medhavi.sarwal@yahoo.com
2
ECE Department, DVIET, Karnal, Haryana, India
Email: shikhakhanna1408@gmail.com
1
Abstract— Fingerprints have long been utilized as a reliable biometric feature for confidential identification.
Fingerprint association mentions to the setback of allocating fingerprints to one of countless pre-specified classes.
Automatic association can be utilized as a pre-processing phase for fingerprint matching, cutting matching period and
intricacy by restricting the find space to a subset of a normally huge database. Fingerprint matching methods can be
mainly categorized as minutiae established and correlation based. Minutiae based method early locates the minutiae
points in a given fingerprint picture and matches their comparative placements in a stored template fingerprint. This
Paper surveys Feature based and wavelet based fingerprint marching of the methods happening in the research
community. We in our work introduce convolution transforms in order to enhance features so as to improve recognition
rate.
Keywords- —Finger Print Recognition, Automated fingerprint recognition systems (AFRSs), Pattern recognition.
1. INTRODUCTION
Fingerprints are the ridge and furrow outlines on the tip of the finger and have been utilized extensively for
confidential identification of people. Figure 1 displays an example of a fingerprint. The biological properties of
fingerprint formation are well understood and fingerprints have been utilized for identification intentions for
centuries. As the commencing of the 20th century, fingerprints have been extensively utilized for identification of
convicts by the assorted forensic departments concerning the world. Due to its convict connotations, a little
people sense uncomfortable in bestowing their fingerprints for identification in civilian applications. Though, as
fingerprint-based biometric arrangements proposal affirmative identification alongside an extremely elevated
degree of assurance, and compact solid state fingerprint sensors can be embedded in assorted arrangements (e.g.,
cellular phones), fingerprint-based authentication is becoming extra and extra accepted in a number of civilian
and business requests such as, welfare disbursement, cellular phone admission, and laptop computer log-in.
Figure 1: Fingerprint Feature selection and extraction
The potential of inexpensive and compact solid state scanners as well as robust fingerprint matchers are two vital
factors in the popularity of fingerprint-based identification systems. Fingerprints additionally have a number of
disadvantages as contrasted to supplementary biometrics. For example, concerning 4% of the populace does not
have good quality fingerprints, manual operatives become usual scratches on their fingers that poses a difficulty
to the matching arrangement, finger skin peels off due to meteorological conditions, fingers develop usual
perpetual creases, provisional creases are industrialized after the labor are immersed in water for a long period,
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IJRREST
INTERNATIONAL JOURNAL OF RESEARCH REVIEW IN
ENGINEERING SCIENCE & TECHNOLOGY
(ISSN 2278–6643)
VOLUME-4, ISSUE-1, APRIL-2015
(Proceedings of “6th National Conference on Make in India PMs Vision : Role of Engineering and Management
Innovations to Achieve this Vision” Organised by Apex Group of Institutions, Karnal, Haryana, India, April-2015)
and grimy fingers cannot be properly imaged alongside the continuing fingerprint sensors. Further, as fingerprints
cannot be seized lacking the user’s vision, they are not suited for precise requests such as surveillance.
2. FINGERPRINT MATCHING
Fingerprint matching methods [1] can be mainly categorized as minutiae established and correlation based.
Minutiae based method early locates the minutiae points in a given fingerprint picture and matches their
comparative placements in a stored template fingerprint. A good quality fingerprint encompasses amid 60 and 80
minutiae, but disparate fingerprints have disparate number of minutiae. The presentation of minutiae-based
methods rely on the precise detection of minutiae points and the use of urbane matching methods to difference
two minutiae fields that experience non-rigid transformations. Correlation based methods [2] difference the globe
outline of ridges and valleys to discern if the ridges in the two fingerprints align. The globe way to fingerprint
representation is normally utilized for indexing and does not proposal reliable fingerprint discrimination. The
ridge construction in a fingerprint can be believed as an oriented sense outlines possessing a dominant spatial
frequency and orientation in a innate neighborhood. The frequency is due to inter ridge-spacing present in a
fingerprint and the orientation is due to the flow outline exhibited by ridges. Most textured pictures encompass a
slim scope of spatial frequencies. For a normal fingerprint pictures scanned at 500 dpi, there is a slight variation
in the spatial frequencies amid disparate fingerprints. This implies that there is an optimal scale (spatial
frequency) for analyzing the fingerprint texture. By seizing the frequency and orientation of ridges in innate spans
in the fingerprint, a different representation of the fingerprint is possible. The counseled scheme early detects the
core point in a fingerprint picture employing two disparate techniques. Core point is described as the north most
point of inner-most ridge line. In habits, the core point corresponds to center of north most loop kind singularity.
A little fingerprint do not encompass loop or whorl singularities, consequently it is tough to delineate core. In that
kind of pictures, core is normally associated alongside the maximum ridge line curvature. Noticing a core point is
not a trivial task; consequently two disparate methods have been utilized to notice optimal core point location. A
circular span concerning the core point is placed and tessellated into 128 sectors.
Figure 2: Optional core point location
The pixel intensities in every single sector are normalized to a steady mean and variance. The circular span is
filtered employing a bank of sixteen Gabor filters [3][4] to produce a set of sixteen filtered images. Gabor filterbanks are a well-recognized method to arrest functional data in specific group bypass channels. Two such
methods have been debated in and. The average definite deviation alongside in a sector quantifies the underlying
ridge construction and is utilized as a feature. The feature vector (2048 benefits in length) is the collection of all
the features, computed from all the 128 sectors, in every single filtered image. The feature vector arrests the
innate data and the arranged enumeration of the tessellation arrests the invariant globe connections amid the
innate patterns. The matching period computes the Euclidean distance amid the two corresponding feature
vectors. It is desirable to attain representations for fingerprints that are translation and rotation invariant. In the
counseled scheme, translation is seized care of by a reference point that is core point across the feature extraction
period and the picture rotation is grasped by a cyclic rotation of the feature benefits in the feature vector. The
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IJRREST
INTERNATIONAL JOURNAL OF RESEARCH REVIEW IN
ENGINEERING SCIENCE & TECHNOLOGY
(ISSN 2278–6643)
VOLUME-4, ISSUE-1, APRIL-2015
(Proceedings of “6th National Conference on Make in India PMs Vision : Role of Engineering and Management
Innovations to Achieve this Vision” Organised by Apex Group of Institutions, Karnal, Haryana, India, April-2015)
features are cyclically rotated to produce feature vectors corresponding to disparate orientations to present the
matching.
3. RELATED WORK
Yi Wang et al., 2011 [5] Recognizing incomplete or partial fingerprints from a colossal fingerprint database
stays a tough trial today. Continuing studies on partial fingerprints focus on one-to-one matching employing
innate ridge details. In this paper, we examine the setback of reclaiming candidate catalogs for matching partial
fingerprints by exploiting globe topological features. Specifically, we counsel an analytical way for
reconstructing the globe topology representation from a partial fingerprint. Firstly, we present an inverse
orientation ideal for delineating the reconstruction problem. Then, we furnish a finished expression for all valid
resolutions to the inverse model.
Rakesh Verma et al., 2011[6] In this research paper rather than little fingerprint identification algorithm (such
as employing Fast Fourier Change (FFT), Minutiae Extraction) that could need so far computation as to be
impractical, Wavelet established algorithm making a low price fingerprint identification system has been
proposed. Wavelet research and its requests to fingerprint verification is one of the fast producing spans for
research in present year. Wavelet theory has been retained in countless fields and requests, such as gesture and
picture processing, contact arrangements, biomedical imaging, radar, air acoustics, hypothetical mathematics,
manipulation arrangement, and endless supplementary areas. Though, the research on requesting the wavelets to
outline credit is yet too weak. As the ridge construction in a fingerprint can be believed as an oriented sense
pattern. The paper proposes a fingerprint credit method established on wavelet established sense outline credit
method. In think to older fingerprint credit method; established on Fast Fourier Change (FFT) and Minutiae
Extraction, the counseled wavelet established method aftermath in elevated credit rates.
Emanuela Marasco et al., 2012 [7] Researchers have demon-started that a little business fingerprint credit
arrangements can be misled after the manmade fingers are allocated on the sensor, i.e., the arrangement
prosperously procedures the following fingerprint pictures thereby al- lowing an antagonist to spoof the
fingerprints of one more individual. Though, at the alike period, countless countermeasures that discriminate
amid live fingerprints and spoof artifacts have been proposed. As a little of these anti-spoofing schemes are
hardware-based, countless software-based ways have been counseled as well. In this paper, we study the works
and present the state-of-the-art in fingerprint anti-spoofing.
Zheng Yang, et al., 2012 [8] Indoor localization is of outstanding significance for a scope of pervasive requests,
appealing countless research efforts in the past decades. Most radio-based resolutions need a procedure of locale
survey, in that wireless signatures of an interested span are annotated alongside their real recorded locations.
Locale survey involves intensive prices on manpower and period, manipulating the applicable constructions of
wireless localization worldwide. In this discover, we examine novel sensors consolidated in present mobile
phones and impact user gestures to craft the wireless chart of a floor design, that is beforehand obtained merely
by locale survey. On this basis, the researchers design LiFS, an indoor localization arrangement established on
off-the-shelf WiFi groundwork and mobile phones. LiFS is used in a workplace constructing obscuring above
1600m2, and its placement is facile and quick as slight human interference is needed. In LiFS, the calibration of
fingerprints is crowd sourced and automatic. Examination aftermath display that LiFS achieves comparable
locale accuracy to preceding ways even lacking locale survey.
Alessandra A Paulino, et al., 2013 [9] An analogy amid the alignment presentation of the counseled algorithm
and the well-known Generalized Hough Change displays the superior presentation of the counseled method.
Researchers have additionally described matching aftermath for two disparate latent fingerprint databases
alongside a colossal background database of concerning 32K rolled prints. We contrasted the presentation of the
counseled matcher alongside three disparate state-of-the-art fingerprint matchers. Experimental aftermath
display that the counseled algorithm performs larger than the three fingerprint matchers utilized in the discover
across all picture qualities. A score-level mixture of the counseled matcher and one of the business matchers
(COTS2) displays a more boost in the matching performance.
Ajita Rattani, et al., 2014 [10] A fingerprint liveness detector is a outline classifier that is utilized to
discriminate a live finger from a fake (spoof) one in the context of an automated fingerprint credit system. Most
liveness detectors are learning-based and rely on a set of training images. Consequently, the presentation of a
liveness detector considerably degrades on encountering spoofs fabricated employing new materials not utilized
across the training stage. To mitigate the protection chance acted by new spoofs, it is vital to automatically
change the liveness detector to new spoofing materials. The target of this work is to design a scheme for
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IJRREST
INTERNATIONAL JOURNAL OF RESEARCH REVIEW IN
ENGINEERING SCIENCE & TECHNOLOGY
(ISSN 2278–6643)
VOLUME-4, ISSUE-1, APRIL-2015
(Proceedings of “6th National Conference on Make in India PMs Vision : Role of Engineering and Management
Innovations to Achieve this Vision” Organised by Apex Group of Institutions, Karnal, Haryana, India, April-2015)
automatic adaptation of a liveness detector to novel spoof materials encountered across the operational phase.
To enable this, a novel-material detector is utilized to ensign input pictures that are deemed to be made of a new
spoofing material. Such flagged pictures are next utilized to retrain the liveness detector. Examinations led on
the LivDet 2011 database counsel (i) a 62% rise in the error rate of continuing liveness detectors after tested
employing new spoof materials, and (ii) upto 46% enhancement in liveness detection presentation across spoof
materials after the counseled adaptive way is used.
Carsten Gottschlich, et al., 2014 [11] Aftermath display that counseled method achieves a comparable average
accuracy alongside the best algorithms on LivDet 2013 employing the alike evaluation protocol; though, this
evaluation procedure limits the evaluation of the algorithm alongside respect to the robustness to mechanism
diversity. We will spread examinations by assessing alongside disparate materials to embody the spoof class and
to clarify interoperability of the counseled liveness detector.
Daxin Tian et al., 2014 [12], in this research paper they counsel a bio-inspired ideal for making handover
decision in heterogeneous wireless networks. It is established on an spread attractor selection ideal, that is
biologically inspired by the self-adaptability and robustness of cellular reply to the adjustments in vibrant
environments. The aim of the counseled ideal is to promise several terminals’ satisfaction by encounter the QoS
necessities of those terminals’ requests, and this ideal additionally endeavors to safeguard the fairness of web
resources allocation, in the temporarily, to enable the QoS-oriented handover decision adaptive to vibrant
wireless environments.
4. CONCLUSION AND FUTURE SCOPE
Fingerprints have long been utilized as a reliable biometric feature for confidential identification. Fingerprint
association mentions to the setback of allocating fingerprints to one of countless pre-specified classes. Automatic
association can be utilized as a pre-processing pace for fingerprint matching, cutting matching period and
intricacy by restricting the find space to a subset of a normally huge database. Automatic fingerprint identification
is one of the most vital biometric technologies. In order to effectually match fingerprints in a colossal database, an
indexing scheme is necessary. Fingerprint association, that mentions to allocating a fingerprint picture into a
number of pre-specified classes, provides a feasible indexing mechanism. In exercise, though colossal intra-class
and tiny interclass variations in globe outline configuration and poor quality of fingerprint pictures make the
association setback extremely difficult. A fingerprint association algorithm needs a robust feature extractor that
ought to be able to reliable remove salient features from input images. A lot work is needed to apply wavelet
based pattern recognition technique for finger print recognition so as to evolve generalized methods independent
of specific requirements and to increase the fingerprint recognition rate. In future, we will work on the same
utilizing convolution transforms.
5. REFERENCES
[1]
Jain, Anil K., Yi Chen, and Meltem Demirkus. "Pores and ridges: High-resolution fingerprint matching using level 3
features." Pattern Analysis and Machine Intelligence, IEEE Transactions on 29, no. 1 (2007): 15-27.
[2] Bazen, Asker M., Gerben TB Verwaaijen, Sabih H. Gerez, Leo PJ Veelenturf, and Berend Jan van der Zwaag. "A
correlation-based fingerprint verification system." (2000).
[3] Jain, Anil K., Salil Prabhakar, Lin Hong, and Sharath Pankanti. "Filterbank-based fingerprint matching." Image
Processing, IEEE Transactions on 9, no. 5 (2000): 846-859.
[4] 4Grigorescu, Simona E., Nicolai Petkov, and Peter Kruizinga. "Comparison of texture features based on Gabor
filters." Image Processing, IEEE Transactions on 11, no. 10 (2002): 1160-1167.
[5] Yi Wang, and Jiankun Hu. "Global ridge orientation modeling for partial fingerprint identification." Pattern Analysis
and Machine Intelligence, IEEE Transactions on 33, no. 1 (2011): 72-87.
[6] Rakesh Verma, and Anuj Goel. "Wavelet application in fingerprint recognition." International Journal of Soft
Computing and Engineering (IJSCE) ISSN (2011): 2231-2307.
[7] Emanuela Marasco, and Carlo Sansone. "Combining perspiration-and morphology-based static features for
fingerprint liveness detection." Pattern Recognition Letters 33, no. 9 (2012): 1148-1156.
[8] Zheng Yang, Chenshu Wu, and Yunhao Liu. "Locating in fingerprint space: wireless indoor localization with little
human intervention." In Proceedings of the 18th annual international conference on Mobile computing and
networking, pp. 269-280. ACM, 2012.
[9] Alessandra A. Paulino, Jianjiang Feng, and Anil K. Jain. "Latent fingerprint matching using descriptor-based Hough
transform." Information Forensics and Security, IEEE Transactions on 8, no. 1 (2013): 31-45.
[10] Ajita Rattani, and Arun Ross. "Automatic adaptation of fingerprint liveness detector to new spoof materials." In
Proceedings of the IEEE International Conference on Biometrics (IJCB'14). 2014.
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IJRREST
INTERNATIONAL JOURNAL OF RESEARCH REVIEW IN
ENGINEERING SCIENCE & TECHNOLOGY
(ISSN 2278–6643)
VOLUME-4, ISSUE-1, APRIL-2015
(Proceedings of “6th National Conference on Make in India PMs Vision : Role of Engineering and Management
Innovations to Achieve this Vision” Organised by Apex Group of Institutions, Karnal, Haryana, India, April-2015)
[11] Carsten Gottschlich, Emanuela Marasco, Allen Y. Yang, and Bojan Cukic. "Fingerprint liveness detection based on
histograms of invariant gradients." Proc. IJCB. Clearwater, FL, USA (2014).
[12] Daxin Tian, Jianshan Zhou, Honggang Qi, Yingrong Lu, Yunpeng Wang, Jian Wang, and Anping He. "A Bio-Inspired
QoS-Oriented Handover Model in Heterogeneous Wireless Networks." Journal of Applied Mathematics 2014 (2014).
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