9 research outputs found
Handmetric verification based on feature-level fusion
Summary This paper presents a multimodal biometric system called "handmetric", which is the fusion of hand based biometrics including palmprint, knuckleprint and hand shape. Moreover, a new framework of FLF (feature-level fusion) is put forward based on subspace analyzes. While verifies that existing feature concatenating methods are instances of the framework, a parameter optimized model using KPCA (kernel principal components analyze) is presented and applied to the verification system. The experiments testify the effectiveness of proposed method
UBSegNet: Unified Biometric Region of Interest Segmentation Network
Digital human identity management, can now be seen as a social necessity, as
it is essentially required in almost every public sector such as, financial
inclusions, security, banking, social networking e.t.c. Hence, in today's
rampantly emerging world with so many adversarial entities, relying on a single
biometric trait is being too optimistic. In this paper, we have proposed a
novel end-to-end, Unified Biometric ROI Segmentation Network (UBSegNet), for
extracting region of interest from five different biometric traits viz. face,
iris, palm, knuckle and 4-slap fingerprint. The architecture of the proposed
UBSegNet consists of two stages: (i) Trait classification and (ii) Trait
localization. For these stages, we have used a state of the art region based
convolutional neural network (RCNN), comprising of three major parts namely
convolutional layers, region proposal network (RPN) along with classification
and regression heads. The model has been evaluated over various huge publicly
available biometric databases. To the best of our knowledge this is the first
unified architecture proposed, segmenting multiple biometric traits. It has
been tested over around 5000 * 5 = 25,000 images (5000 images per trait) and
produces very good results. Our work on unified biometric segmentation, opens
up the vast opportunities in the field of multiple biometric traits based
authentication systems.Comment: 4th Asian Conference on Pattern Recognition (ACPR 2017
Palmprint Recognition Using Different Level of Information Fusion
The aim of this paper is to investigate a fusion approach suitable for palmprint recognition. Several number of fusion stageis analyse such as feature, matching and decision level. Fusion at feature level is able to increase discrimination power in the feature space by producing high dimensional fuse feature vector. Fusion at matching score level utilizes the matching output from different classifier to form a single value for decision process. Fusion at decision level on the other hand utilizes minimal information from a different matching process and the integration at this stage is less complex compare to other approach. The analysis shows integration at feature level produce the best recognition rates compare to the other method
Building a Strong Undergraduate Research Culture in African Universities
Africa had a late start in the race to setting up and obtaining universities with research quality fundamentals. According to Mamdani [5], the first colonial universities were few and far between: Makerere in East Africa, Ibadan and Legon in West Africa. This last place in the race, compared to other continents, has had tremendous implications in the development plans for the continent. For Africa, the race has been difficult from a late start to an insurmountable litany of problems that include difficulty in equipment acquisition, lack of capacity, limited research and development resources and lack of investments in local universities. In fact most of these universities are very recent with many less than 50 years in business except a few. To help reduce the labor costs incurred by the colonial masters of shipping Europeans to Africa to do mere clerical jobs, they started training ―workshops‖ calling them technical or business colleges. According to Mamdani, meeting colonial needs was to be achieved while avoiding the ―Indian disease‖ in Africa -- that is, the development of an educated middle class, a group most likely to carry the virus of nationalism. Upon independence, most of these ―workshops‖ were turned into national ―universities‖, but with no clear role in national development. These national ―universities‖ were catering for children of the new African political elites. Through the seventies and eighties, most African universities were still without development agendas and were still doing business as usual. Meanwhile, governments strapped with lack of money saw no need of putting more scarce resources into big white elephants. By mid-eighties, even the UN and IMF were calling for a limit on funding African universities. In today‘s African university, the traditional curiosity driven research model has been replaced by a market-driven model dominated by a consultancy culture according to Mamdani (Mamdani, Mail and Guardian Online). The prevailing research culture as intellectual life in universities has been reduced to bare-bones classroom activity, seminars and workshops have migrated to hotels and workshop attendance going with transport allowances and per diems (Mamdani, Mail and Guardian Online). There is need to remedy this situation and that is the focus of this paper
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