An Efficient Fingerprint Identification using Neural Network and BAT Algorithm

The uniqueness, firmness, public recognition, and its minimum risk of intrusion made fingerprint is an expansively used personal authentication metrics. Fingerprint technology is a biometric technique used to distinguish persons based on their physical traits. Fingerprint based authentication schemes are becoming increasingly common and usage of these in fingerprint security schemes, made an objective to the attackers. The repute of the fingerprint image controls the sturdiness of a fingerprint authentication system. We intend for an effective method for fingerprint classification with the help of soft computing methods. The proposed classification scheme is classified into three phases. The first phase is preprocessing in which the fingerprint images are enhanced by employing median filters. After noise removal histogram equalization is achieved for augmenting the images. The second stage is the feature Extraction phase in which numerous image features such as Area, SURF, holo entropy, and SIFT features are extracted. The final phase is classification using hybrid Neural for classification of fingerprint as fake or original. The neural network is unified with BAT algorithm for optimizing the weight factor

Evolutionary Algorithms Application Analysis in Biometric Systems

Wide usage of biometric information for person identity verification purposes, terrorist acts prevention measures and authenticationprocess simplification in computer systems has raised significant attention to reliability and efficiency of biometricsystems. Modern biometric systems still face many reliability and efficiency related issues such as reference databasesearch speed, errors while recognizing of biometric information or automating biometric feature extraction. Current scientificinvestigations show that application of evolutionary algorithms may significantly improve biometric systems. In thisarticle we provide a comprehensive review of main scientific research done in sphere of evolutionary algorithm applicationfor biometric system parameter improvement

Offline Handwritten Signature Verification - Literature Review

The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification systems is to discriminate if a given signature is genuine (produced by the claimed individual), or a forgery (produced by an impostor). This has demonstrated to be a challenging task, in particular in the offline (static) scenario, that uses images of scanned signatures, where the dynamic information about the signing process is not available. Many advancements have been proposed in the literature in the last 5-10 years, most notably the application of Deep Learning methods to learn feature representations from signature images. In this paper, we present how the problem has been handled in the past few decades, analyze the recent advancements in the field, and the potential directions for future research.Comment: Accepted to the International Conference on Image Processing Theory, Tools and Applications (IPTA 2017

On Generative Adversarial Network Based Synthetic Iris Presentation Attack And Its Detection

Human iris is considered a reliable and accurate modality for biometric recognition due to its unique texture information. Reliability and accuracy of iris biometric modality have prompted its large-scale deployment for critical applications such as border control and national identification projects. The extensive growth of iris recognition systems has raised apprehensions about the susceptibility of these systems to various presentation attacks. In this thesis, a novel iris presentation attack using deep learning based synthetically generated iris images is presented. Utilizing the generative capability of deep convolutional generative adversarial networks and iris quality metrics, a new framework, named as iDCGAN is proposed for creating realistic appearing synthetic iris images. In-depth analysis is performed using quality score distributions of real and synthetically generated iris images to understand the effectiveness of the proposed approach. We also demonstrate that synthetically generated iris images can be used to attack existing iris recognition systems. As synthetically generated iris images can be effectively deployed in iris presentation attacks, it is important to develop accurate iris presentation attack detection algorithms which can distinguish such synthetic iris images from real iris images. For this purpose, a novel structural and textural feature-based iris presentation attack detection framework (DESIST) is proposed. The key emphasis of DESIST is on developing a unified framework for detecting a medley of iris presentation attacks, including synthetic iris. Experimental evaluations showcase the efficacy of the proposed DESIST framework in detecting synthetic iris presentation attacks

User identification system for inked fingerprint pattern based on central moments

The use of the fingerprint recognition has been and remains very important in many security applications and licensing systems. Fingerprint recognition is required in many areas such as licensing access to networks, corporate computers and organizations. In this paper, the system of fingerprint recognition that can be used in several cases of fingerprint such as being rounded at an angle by a randomly inked fingerprint on paper. So, fingerprint image is tooked at a different angle in order to identify the owner of the ink fingerprint. This method involves two working levels. The first one, the fingerprint pattern's shape features are calculated based on the central moments of each image being listed on a regular basis with three states rotation. Each image is rotated at a specified angle. In the second level, the fingerprint holder entered is identified using the previously extracted shape features and compared to the three local databases content of three rotation states. When applied the method for several persons by taken their inked fingerprint on the paper, the accuracy of the system in identifying the owner of the fingerprint after rotation states were close to 83.71

Non-ideal iris recognition

Of the many biometrics that exist, iris recognition is finding more attention than any other due to its potential for improved accuracy, permanence, and acceptance. Current iris recognition systems operate on frontal view images of good quality. Due to the small area of the iris, user co-operation is required. In this work, a new system capable of processing iris images which are not necessarily in frontal view is described. This overcomes one of the major hurdles with current iris recognition systems and enhances user convenience and accuracy. The proposed system is designed to operate in two steps: (i) preprocessing and estimation of the gaze direction and (ii) processing and encoding of the rotated iris image. Two objective functions are used to estimate the gaze direction. Later, the off-angle iris image undergoes geometric transformations involving the estimated angle and is further processed as if it were a frontal view image. Two methods: (i) PCA and (ii) ICA are used for encoding. Three different datasets are used to quantify performance of the proposed non-ideal recognition system

Application of variations of non-linear CCA for feature selection in drug sensitivity prediction

Cancer arises due to the genetic alteration in patient DNA. Many studies indicate the fact that these alterations vary among patients and can affect the therapeutic effect of cancer treatment dramatically. Therefore, extensive studies focus on understanding these alterations and their effects. Pre-clinical models play an important role in cancer drug discovery and cancer cell lines are one of the main ingredients of these pre-clinical studies which can capture many different aspects of multi-omics properties of cancer cells. However, the assessment of cancer cell line responses to different drugs is faulty and laborious. Therefore, in-silico models, which perform accurate prediction of drug sensitivity values, enhance cancer drug discovery. In the past decade, many computational methods achieved high performances by studying similarity between cancer cell lines and drug compounds and used them to obtain an accurate predictive model for unknown instances. In this thesis, we study the effect of non-linear feature selection through two variations of canonical correlation analysis, KCCA, and HSIC-SCCA, on the prediction of drug sensitivity. To estimate the performance of these features we use pairwise kernel ridge regression to predict the drug sensitivity, measured as IC50 values. The data set under study is a subset of Genomics of Drug Sensitivity in Cancer comprise of 124 cell lines and 124 drug compounds. The high diversity between cell lines and drug compound samples and the high dimension of data matrices reduce the accuracy of the model obtained by pairwise kernel ridge regression. This accuracy reduced by employing HSIC-SCCA method as a dimension reduction step since the HSIC-SCCA method increased the differences among the samples by employing different projection vectors for samples in different folds of cross-validation. Therefore, the obtained variables are rotated to provide more homogeneous samples. This step slightly improved the accuracy of the model