13 research outputs found
The fundamentals of unimodal palmprint authentication based on a biometric system: A review
Biometric system can be defined as the automated method of identifying or authenticating the identity of a living person based on physiological or behavioral traits. Palmprint biometric-based authentication has gained considerable attention in recent years. Globally, enterprises have been exploring biometric authorization for some time, for the purpose of security, payment processing, law enforcement CCTV systems, and even access to offices, buildings, and gyms via the entry doors. Palmprint biometric system can be divided into unimodal and multimodal. This paper will investigate the biometric system and provide a detailed overview of the palmprint technology with existing recognition approaches. Finally, we introduce a review of previous works based on a unimodal palmprint system using different databases
Palmprint identification using restricted fusion
2008-2009 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe
LEARNING-FREE DEEP FEATURES FOR MULTISPECTRAL PALM-PRINT CLASSIFICATION
The feature extraction step is a major and crucial step in analyzing and understanding raw data as it has a considerable impact on the system accuracy. Unfortunately, despite the very acceptable results obtained by many handcrafted methods, they can have difficulty representing the features in the case of large databases or with strongly correlated samples. In this context, we proposed a new, simple and lightweight method for deep feature extraction. Our method can be configured to produce four different deep features, each controlled to tune the system accuracy. We have evaluated the performance of our method using a multispectral palmprint based biometric system and the experimental results, using the CASIA database, have shown that our method has high accuracy compared to many current handcrafted feature extraction methods and many well known deep learning based methods
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
RPG-Palm: Realistic Pseudo-data Generation for Palmprint Recognition
Palmprint recently shows great potential in recognition applications as it is
a privacy-friendly and stable biometric. However, the lack of large-scale
public palmprint datasets limits further research and development of palmprint
recognition. In this paper, we propose a novel realistic pseudo-palmprint
generation (RPG) model to synthesize palmprints with massive identities. We
first introduce a conditional modulation generator to improve the intra-class
diversity. Then an identity-aware loss is proposed to ensure identity
consistency against unpaired training. We further improve the B\'ezier palm
creases generation strategy to guarantee identity independence. Extensive
experimental results demonstrate that synthetic pretraining significantly
boosts the recognition model performance. For example, our model improves the
state-of-the-art B\'ezierPalm by more than and in terms of
TAR@FAR=1e-6 under the and Open-set protocol. When accessing only
of the real training data, our method still outperforms ArcFace with
real training data, indicating that we are closer to real-data-free
palmprint recognition.Comment: 12 pages,8 figure
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
Biometric face recognition using multilinear projection and artificial intelligence
PhD ThesisNumerous problems of automatic facial recognition in the linear and multilinear
subspace learning have been addressed; nevertheless, many difficulties remain. This
work focuses on two key problems for automatic facial recognition and feature
extraction: object representation and high dimensionality.
To address these problems, a bidirectional two-dimensional neighborhood preserving
projection (B2DNPP) approach for human facial recognition has been developed.
Compared with 2DNPP, the proposed method operates on 2-D facial images and
performs reductions on the directions of both rows and columns of images.
Furthermore, it has the ability to reveal variations between these directions. To further
improve the performance of the B2DNPP method, a new B2DNPP based on the
curvelet decomposition of human facial images is introduced. The curvelet multi-
resolution tool enhances the edges representation and other singularities along curves,
and thus improves directional features. In this method, an extreme learning machine
(ELM) classifier is used which significantly improves classification rate. The proposed
C-B2DNPP method decreases error rate from 5.9% to 3.5%, from 3.7% to 2.0% and
from 19.7% to 14.2% using ORL, AR, and FERET databases compared with 2DNPP.
Therefore, it achieves decreases in error rate more than 40%, 45%, and 27%
respectively with the ORL, AR, and FERET databases.
Facial images have particular natural structures in the form of two-, three-, or even
higher-order tensors. Therefore, a novel method of supervised and unsupervised
multilinear neighborhood preserving projection (MNPP) is proposed for face
recognition. This allows the natural representation of multidimensional images 2-D, 3-D
or higher-order tensors and extracts useful information directly from tensotial data
rather than from matrices or vectors. As opposed to a B2DNPP which derives only two
subspaces, in the MNPP method multiple interrelated subspaces are obtained over
different tensor directions, so that the subspaces are learned iteratively by unfolding the
tensor along the different directions. The performance of the MNPP has performed in
terms of the two modes of facial recognition biometrics systems of identification and
verification. The proposed supervised MNPP method achieved decrease over 50.8%,
75.6%, and 44.6% in error rate using ORL, AR, and FERET databases respectively,
compared with 2DNPP. Therefore, the results demonstrate that the MNPP approach
obtains the best overall performance in various learning scenarios