3 research outputs found
Multispectral Palmprint Encoding and Recognition
Palmprints are emerging as a new entity in multi-modal biometrics for human
identification and verification. Multispectral palmprint images captured in the
visible and infrared spectrum not only contain the wrinkles and ridge structure
of a palm, but also the underlying pattern of veins; making them a highly
discriminating biometric identifier. In this paper, we propose a feature
encoding scheme for robust and highly accurate representation and matching of
multispectral palmprints. To facilitate compact storage of the feature, we
design a binary hash table structure that allows for efficient matching in
large databases. Comprehensive experiments for both identification and
verification scenarios are performed on two public datasets -- one captured
with a contact-based sensor (PolyU dataset), and the other with a contact-free
sensor (CASIA dataset). Recognition results in various experimental setups show
that the proposed method consistently outperforms existing state-of-the-art
methods. Error rates achieved by our method (0.003% on PolyU and 0.2% on CASIA)
are the lowest reported in literature on both dataset and clearly indicate the
viability of palmprint as a reliable and promising biometric. All source codes
are publicly available.Comment: Preliminary version of this manuscript was published in ICCV 2011. Z.
Khan A. Mian and Y. Hu, "Contour Code: Robust and Efficient Multispectral
Palmprint Encoding for Human Recognition", International Conference on
Computer Vision, 2011. MATLAB Code available:
https://sites.google.com/site/zohaibnet/Home/code
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%
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