8 research outputs found
Palm print verification based deep learning
In this paper, we consider a palm print characteristic which has taken wide attentions in recent studies. We focused on palm print verification problem by designing a deep network called a palm convolutional neural network (PCNN). This network is adapted to deal with two-dimensional palm print images. It is carefully designed and implemented for palm print data. Palm prints from the Hong Kong Polytechnic University Contact-free (PolyUC) 3D/2D hand images dataset are applied and evaluated. The results have reached the accuracy of 97.67%, this performance is superior and it shows that our proposed method is efficient
Deep fingerprint classification network
Fingerprint is one of the most well-known biometrics that has been used for personal recognition. However, faked fingerprints have become the major enemy where they threat the security of this biometric. This paper proposes an efficient deep fingerprint classification network (DFCN) model to achieve accurate performances of classifying between real and fake fingerprints. This model has extensively evaluated or examined parameters. Total of 512 images from the ATVS-FFp_DB dataset are employed. The proposed DFCN achieved high classification performance of 99.22%, where fingerprint images are successfully classified into their two categories. Moreover, comparisons with state-of-art approaches are provided
Interpreting Arabic Sign Alphabet by using the Deep Learning
Sign Language (SL) is a communication method between people. It is an essential language; especially for people who are speech impaired and hearing impaired, it can be considered as their mother tongues. Hand gestures form the nonverbal communication of this language. We focus on interpreting Arabic Sign Alphabet (ASA) in this study and, as a case study, the recognition of alphabet in Iraqi Sign Language (IrSL) is carried out with the help of specialists from the “Al-Amal Institute for the Deaf and Dumb”. A new ASA dataset of various hand gestures was created and adopted. In addition, a deep learning model named the Deep Arabic Sign Alphabet (DASA) is proposed, which is a developed version of the Convolutional Neural Network (CNN). It can efficiently interpret the ASA, achieving a high interpretation accuracy of 95.25%
Efficient finger segmentation robust to hand alignment in imaging with application to human verification
Finger segmentation is the first challenging step in a Finger Texture (FT) recognition system. We propose an efficient finger segmentation method to address the problem of variation in the alignment of the hand. A scanning line is suggested to detect the hand position and determine the main characteristics of the fingers. Furthermore, an adaptive threshold and adaptive rotation step are exploited. The proposed segmentation scheme is then integrated into a powerful human verification scheme based on a finger Feature Level Fusion (FLF) method with the Probabilistic Neural Network (PNN). Three databases are employed for evaluation: IIT Delhi, PolyU3D2D and spectral 460 from the CASIA Multi-Spectral Palmprint database. The proposed method has efficiently isolated the fingers and resulted in best Equal Error Rate (EER) values for the three databases of 2.03%, 0.68% and 5%, respectively. Moreover, comparisons with related work are provided in this study
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