42 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
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
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%
A Framework for Verification in Contactless Secure Physical Access Control and Authentication Systems
Biometrics is one of the very popular techniques in user identification for accessing institutions and logging into attendance systems. Currently, some of the existing biometric techniques such as the use of fingerprints are unpopular due to COVID-19 challenges. This paper identifies the components of a framework for secure contactless access authentication. The researcher selected 50 journals from Google scholar which were used to analyze the various components used in a secure contactless access authentication framework. The methodology used for research was based on the scientific approach of research methodology that mainly includes data collection from the 50 selected journals, analysis of the data and assessment of results. The following components were identified: database, sensor camera, feature extraction methods, matching and decision algorithm. Out of the considered journals the most used is CASIA database at 40%, CCD Sensor camera with 56%, Gabor feature extraction method at 44%, Hamming distance for matching at 100% and PCA at 100% was used for decision making. These findings will assist the researcher in providing a guide on the best suitable components. Various researchers have proposed an improvement in the current security systems due to integrity and security problems
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
Vascular Innovation Science
Vascular Innovation science are a standout amongst the most approaching innovations which is very secure. In this paperi introduces a study of machine learning techniques utilized for Vascular innovation development applications, and distinguishes significant exploration issues. Basic mission assets and applications require instruments to recognize when honest to goodness clients attempt to abuse their benefits; unquestionably biometrics serves to give such administrations
A NOVEL PERSONAL AUTHENTICATION USING KNUCKLE MULTISPECTRAL PATTERN
ABSTRACT With the increased use of biometrics for identity verification, there have been similar increases in the use of unimodal biometric system. The finger knuckle print recognition is one of the newest biometric techniques research today. In this paper, one of the reliable and robust personal identification approaches using finger knuckle print is presented. Many researchers are going on in face, finger print and iris recognition and which finds its usage in many applications. These biometric which find its usage in many applications are easily duplicated for fraudulent activities. But the finger knuckle print recognition is the unique pattern to identify the individuality at a high level of accuracy. This paper proposes new algorithms for finger knuckle print recognition using SIFT algorithm and this algorithm presents, extracting a new original constant features from images As the proposed method matches the different angles of finger knuckle print with the database, its reliability is very high when compared to other biometrics. The features of SIFT which are invariant to image scale and rotation, are shown to provide robust matching across a substantial range of fine distortion, change in 3D viewpoint, addition of noise, and change in illuminance. The features are highly distinctive, in the sense that a single feature could be correctly matched with high probability against a large database of features from many images
Machine Learning Methods for Human Identification from Dorsal Hand Images
Person identification is a process that uniquely identifies an individual based on physical or behavioural traits. This study investigates methods for the analysis of images of the human hand, focusing on their uniqueness and potential use for human identification. The human hand has significant and distinctive characteristics, and is highly complex and interesting, yet it has not been explored in much detail, particularly in the context of the contemporary high level of digitalisation and, more specifically, the advances in artificial intelligence (AI), machine learning (ML) and computer vision (CV). This research area is highly multi-disciplinary, involving anatomists, anthropologists, bioinformaticians, image analysts and, increasingly, computer scientists. A growing pool of advanced methods based on AI, ML and CV can benefit and relate directly to a better representation of the human hand in computer analysis. Historically, the research methods in this area relied on ‘handcrafted’ features such as the local binary pattern (LBP) and histogram of gradient (HOG) extraction, which necessitated human intervention. However, such approaches struggle to encode the human hand in variable conditions effectively, because of the change in camera viewpoint, hand pose, rotation, image quality, and self-occlusion. Thus, their performance is limited. Recently, there has been a surge of interest in deep learning neural network (DLNN) approaches, specifically convolutional neural networks (CNNs), due to the highly accurate results achieved in many applications and the wide availability of images. This work investigates advanced methods based on ML and DLNN for segmenting hand images with various rotation changes into different patches (e.g., knuckles and fingernails). The thesis focuses on developing ML methods like pre-trained CNN models on the 'ImageNet' dataset to learn the underlying structure of the human hand by extracting robust features from hand images with diverse conditions of rotation, and image quality. Also, this study investigates fine-tuning the pre-trained models of DLNN on subsets of hand images, as well as using various similarity metrics to find the best match of the individual’s hand. Furthermore, this work explores different types of ensemble learning or fusions, those of different region and similarity metrics to improve human identification results. This thesis also presents a study of a Siamese network on sub-images or segments of human dorsal hands for identification tasks. All presented approaches are compared with the state-of-the-art methods. This study advances the understanding of variations in and the uniqueness of humans using patches of their hands (e.g., different types of knuckles and fingernails). Lastly, it compares the matching performances of the left- and right-hand patches using various hand datasets and investigates whether the fingernail produces better identification results than the knuckles. This research shows that the proposed framework for person identification based on hand components led to better person identification results. The framework consists of vital feature extractions based on deep learning neural network (DLNN) and similarity metrics. These elements enhanced the performance. Also, the fingernails' shape performed better than other hand components, including the base, major, and minor knuckles. The left hand can be more distinguishable to individuals than the right hand. The fine-tuning of the hand components and ensemble learning improved the identification results
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