460 research outputs found
Fusion of geometric and texture features for finger knuckle surface recognition
AbstractHand-based biometrics plays a significant role in establishing security for real-time environments involving human interaction and is found to be more successful in terms of high speed and accuracy. This paper investigates on an integrated approach for personal authentication using Finger Back Knuckle Surface (FBKS) based on two methodologies viz., Angular Geometric Analysis based Feature Extraction Method (AGFEM) and Contourlet Transform based Feature Extraction Method (CTFEM). Based on these methods, this personal authentication system simultaneously extracts shape oriented feature information and textural pattern information of FBKS for authenticating an individual. Furthermore, the proposed geometric and textural analysis methods extract feature information from both proximal phalanx and distal phalanx knuckle regions (FBKS), while the existing works of the literature concentrate only on the features of proximal phalanx knuckle region. The finger joint region found nearer to the tip of the finger is called distal phalanx region of FBKS, which is a unique feature and has greater potentiality toward identification. Extensive experiments conducted using newly created database with 5400 FBKS images and the obtained results infer that the integration of shape oriented features with texture feature information yields excellent accuracy rate of 99.12% with lowest equal error rate of 1.04%
Signal processing and machine learning techniques for human verification based on finger textures
PhD ThesisIn recent years, Finger Textures (FTs) have attracted considerable
attention as potential biometric characteristics. They can provide
robust recognition performance as they have various human-speci c
features, such as wrinkles and apparent lines distributed along the
inner surface of all ngers. The main topic of this thesis is verifying
people according to their unique FT patterns by exploiting signal
processing and machine learning techniques.
A Robust Finger Segmentation (RFS) method is rst proposed to
isolate nger images from a hand area. It is able to detect the ngers
as objects from a hand image. An e cient adaptive nger
segmentation method is also suggested to address the problem of
alignment variations in the hand image called the Adaptive and Robust
Finger Segmentation (ARFS) method.
A new Multi-scale Sobel Angles Local Binary Pattern (MSALBP)
feature extraction method is proposed which combines the Sobel
direction angles with the Multi-Scale Local Binary Pattern (MSLBP).
Moreover, an enhanced method called the Enhanced Local Line Binary
Pattern (ELLBP) is designed to e ciently analyse the FT patterns. As
a result, a powerful human veri cation scheme based on nger Feature
Level Fusion with a Probabilistic Neural Network (FLFPNN) is
proposed. A multi-object fusion method, termed the Finger
Contribution Fusion Neural Network (FCFNN), combines the
contribution scores of the nger objects.
The veri cation performances are examined in the case of missing FT
areas. Consequently, to overcome nger regions which are poorly
imaged a method is suggested to salvage missing FT elements by
exploiting the information embedded within the trained Probabilistic
Neural Network (PNN). Finally, a novel method to produce a Receiver
Operating Characteristic (ROC) curve from a PNN is suggested.
Furthermore, additional development to this method is applied to
generate the ROC graph from the FCFNN.
Three databases are employed for evaluation: The Hong Kong
Polytechnic University Contact-free 3D/2D (PolyU3D2D), Indian
Institute of Technology (IIT) Delhi and Spectral 460nm (S460) from
the CASIA Multi-Spectral (CASIAMS) databases. Comparative
simulation studies con rm the e ciency of the proposed methods for
human veri cation.
The main advantage of both segmentation approaches, the RFS and
ARFS, is that they can collect all the FT features. The best results
have been benchmarked for the ELLBP feature extraction with the
FCFNN, where the best Equal Error Rate (EER) values for the three
databases PolyU3D2D, IIT Delhi and CASIAMS (S460) have been
achieved 0.11%, 1.35% and 0%, respectively. The proposed salvage
approach for the missing feature elements has the capability to enhance
the veri cation performance for the FLFPNN. Moreover, ROC graphs
have been successively established from the PNN and FCFNN.the ministry of higher
education and scientific research in Iraq (MOHESR); the Technical
college of Mosul; the Iraqi Cultural Attach e; the active people in the
MOHESR, who strongly supported Iraqi students
Patterns Identification of Finger Outer Knuckles by Utilizing Local Directional Number
Finger Outer Knuckle (FOK) is a distinctive biometric that has grown in popularity recently. This results from its inborn qualities such as stability, protection, and specific anatomical patterns. Applications for the identification of FOK patterns include forensic investigations, access control systems, and personal identity. In this study, we suggest a method for identifying FOK patterns using Local Directional Number (LDN) codes produced from gradient-based compass masks. For the FOK pattern matching, the suggested method uses two asymmetric masks—Kirsch and Gaussian derivative—to compute the edge response and extract LDN codes. To calculate edge response on the pattern, an asymmetric compass mask made from the Gaussian derivative mask is created by rotating the Kirsch mask by 45 degrees to provide edge response in eight distinct directions. The edge response of each mask and the combination of dominating vector numbers are examined during the LDN code-generating process. A distance metric can be used to compare the LDN code\u27s condensed representation of the FOK pattern to the original for matching purposes. On the Indian Institute of Technology Delhi Finger Knuckle (IITDFK) database, the efficiency of the suggested procedure is assessed. The data show that the suggested strategy is effective, with an Equal Error Rate (EER) of 10.78%. This value performs better than other EER values when compared to different approaches
A Robust Finger Knuckle Print Authentication using Topothesy and fractal dimension
This paper presents the finger knuckle based biometric authentication system using the approaches like structure entropy, GSHP (Gaussian smoothed High pass), GSOD (Gaussian Smoothed Oriented Directives) and also the well known method for surface roughness measurement called the fractal profiles represented by Topothesy and fractal dimension which describe  not only the roughness but also the affine self similarity. We have also implemented Daisy descriptor for the representation of texture. The results of fractal parameters along with the refined scores are comparable to those of the compcode and impcompcode
An Evaluation of Score Level Fusion Approaches for Fingerprint and Finger-vein Biometrics
Biometric systems have to address many requirements, such as large population
coverage, demographic diversity, varied deployment environment, as well as
practical aspects like performance and spoofing attacks. Traditional unimodal
biometric systems do not fully meet the aforementioned requirements making them
vulnerable and susceptible to different types of attacks. In response to that,
modern biometric systems combine multiple biometric modalities at different
fusion levels. The fused score is decisive to classify an unknown user as a
genuine or impostor. In this paper, we evaluate combinations of score
normalization and fusion techniques using two modalities (fingerprint and
finger-vein) with the goal of identifying which one achieves better improvement
rate over traditional unimodal biometric systems. The individual scores
obtained from finger-veins and fingerprints are combined at score level using
three score normalization techniques (min-max, z-score, hyperbolic tangent) and
four score fusion approaches (minimum score, maximum score, simple sum, user
weighting). The experimental results proved that the combination of hyperbolic
tangent score normalization technique with the simple sum fusion approach
achieve the best improvement rate of 99.98%.Comment: 10 pages, 5 figures, 3 tables, conference, NISK 201
Deep finger texture learning for verifying people
Finger Texture (FT) is currently attracting significant attentions in the area of human recognition. Finger texture covers the area between the lower knuckle of the finger and the upper phalanx before the fingerprint. It involves rich features which can be efficiently used as a biometric characteristic. In this paper, we contribute to this growing area by proposing a new verification
approach, i.e., Deep Finger Texture Learning (DFTL). To the best of our knowledge, this is the first time that deep learning is employed for recognizing people by using the FT characteristic. Four databases have been used to evaluate the proposed method: the Hong Kong Polytechnic University Contact-free 3D/2D (PolyU2D), Indian Institute of Technology Delhi (IITD), CASIA Blue spectral (CASIA-BLU) corresponding to spectral 460nm and CASIA White spectral (CASIA-WHT) from the CASIA Multi-Spectral images database. The obtained results have shown superior performance compared with recent literature. The Verification Accuracies (VAs) have attained 100%, 98.65%, 100% and 98% for the four databases of PolyU2D, IITD, CASIA-BLU and CASIA-WHT, respectively
Finger Knuckle Analysis: Gabor Vs DTCWT
Knuckle biometrics is one of the current trends in biometric human identification which offers a reliable solution for verification. This paper analysis FKP recognition based on the behaviour of two different filtering and classification methods. Firstly, Gabor Filter Banks techniques are applied for finger knuckle print recognition and then the same database is analysed against Dual Tree Complex Wavelet Transform technique. The experiment is evaluated to identify finger knuckle images using PolyU FKP database of 7920 images. Finally, these two different systems are compared for false acceptance rate FAR, true acceptance, false rejection rate FRR and true rejection. Extensive experiments are performed to evaluate both the techniques, and experimental results show the pros and cons of using both the techniques for specific applications.
DOI: 10.17762/ijritcc2321-8169.150518
Finger texture verification systems based on multiple spectrum lighting sensors with four fusion levels
Finger Texture (FT) is one of the most recent attractive biometric characteristic. It refers to a finger skin area which is restricted between the fingerprint and the palm print (just after including the lower knuckle). Different specifications for the FT can be obtained by employing multiple images spectrum of lights. Individual verification systems are established in this paper by using multiple spectrum FT specifications. The key idea here is that by combining two various spectrum lightings of FTs, high personal recognitions can be attained. Four types of fusion will be listed and explained here: Sensor Level Fusion (SLF), Feature Level Fusion (FLF), Score Level Fusion (ScLF) and Decision Level Fusion (DLF). Each fusion method is employed, examined for different rules and analysed. Then, the best performance procedure is benchmarked to be considered. From the database of Multiple Spectrum CASIA (MSCASIA), FT images have been collected. Two types of spectrum lights have been exploited (the wavelength of 460 nm, which represents a Blue (BLU) light, and the White (WHT) light). Supporting comparisons were performed, including the state-of-the-art. Best recognition performance was recorded for the FLF based concatenation rule by improving the Equal Error Rate (EER) percentages from 5% for the BLU and 7% for the WHT to 2%
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