Offline signature verification using classifier combination of HOG and LBP features

We present an offline signature verification system based on a signature’s local histogram features. The signature is divided into zones using both the Cartesian and polar coordinate systems and two different histogram features are calculated for each zone: histogram of oriented gradients (HOG) and histogram of local binary patterns (LBP). The classification is performed using Support Vector Machines (SVMs), where two different approaches for training are investigated, namely global and user-dependent SVMs. User-dependent SVMs, trained separately for each user, learn to differentiate a user’s signature from others, whereas a single global SVM trained with difference vectors of query and reference signatures’ features of all users, learns how to weight dissimilarities. The global SVM classifier is trained using genuine and forgery signatures of subjects that are excluded from the test set, while userdependent SVMs are separately trained for each subject using genuine and random forgeries. The fusion of all classifiers (global and user-dependent classifiers trained with each feature type), achieves a 15.41% equal error rate in skilled forgery test, in the GPDS-160 signature database without using any skilled forgeries in training

Метод обнаружения искусственного изменения папиллярных узоров отпечатков пальцев на основе когерентности поля ориентаций

Одной из актуальных проблем, связанных с безопасностью биометрических технологий, является обнаружение фальшивых и подвергшихся искусственному изменению папиллярных узоров (ИИПУ) отпечатков пальцев. Разработан эффективный метод обнаружения ИИПУ отпечатков пальцев на основе модифицированной модели когерентности поля ориентаций. Результаты экспериментов показывают, что метод хорошо выявляет изображения ИИПУ отпечатков пальцев. Предложенный метод не требует дополнительной обработки и использует результаты вычислений в традиционных блоках существующих систем распознавания отпечатков пальцев.Широке застосування біометричних технологій виявляє різні проблеми, пов’язані з їх безпекою. Однією з актуальних проблем є виявлення фальшивих і таких, що зазнали штучних змін, папілярних візерунків відбитків пальців. Розроблено ефективний метод виявлення штучної зміни папілярних візерунків відбитків пальців на основі модифікованої моделі когерентності поля орієнтацій. Результати експериментів показують, що метод добре виявляє зображення штучної зміни папілярних візерунків відбитків пальців. Запропонований метод не вимагає додаткової обробки і використовує результати обчислень в традиційних блоках існуючих систем розпізнавання відбитків пальців.Widespread use of biometric technologies determines various problems related to their security. One of the important problems is detection of forged and altered fingerprints. An efficient method for altered fingerprints detection on the basis of the modified model of the orientation field coherence is discovered. The results of experiments show that the method detects altered fingerprints well. The proposed method does not require additional processing resources and it uses the results of the traditional blocks of existing fingerprint recognition systems

Offline Signature Verification based on Euclidean distance using Support Vector Machine

In this project, a support vector machine is developed for identity verification of offline signature based on the matrices derived through Euclidean distance. A set of signature samples are collected from 35 different people. Each person gives his 15 different copies of signature and then these signature samples are scanned to have softcopy of them to train SVM. These scanned signature images are then subjected to a number of image enhancement operations like binarization, complementation, filtering, thinning, edge detection and rotation. On the basis of 15 original signature copies from each individual, Euclidean distance is calculated. And every tested image is compared with the range of Euclidean distance. The values from the ED are fed to the support vector machine which draws a hyper plane and classifies the signature into original or forged based on a particular feature value

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

Biometric recognition based on the texture along palmprint lines

Tese de Mestrado Integrado. Bioengenharia. Faculdade de Engenharia. Universidade do Porto. 201

Features Mapping Based Human Gait Recognition

Gait recognition is the term used for detection of Human based on the features. The Feature extraction and Feature Mapping is the main aspect to recognize the Gestures from the Database of features. Recognition of any individual is a task to identify people. Human recognition methods such as face, fingerprints, and iris generally require a cooperative subject, physical contact or close proximity. These methods are not able to recognize an individual at a distance therefore recognition using gait is relatively new biometric technique without these disadvantages. Human identification using Gait is method to identify an individual by the way he walk or manner of moving on foot. Gait recognition is a type of biometric recognition and related to the behavioral characteristics of biometric recognition. Gait offers ability of distance recognition or at low resolution. This project aims to recognize an individual using his gait features. However the majority of current approaches are model free which is simple and fast but we will use model based approach for feature extraction and for matching of parameters with database sequences. After matching of Features, the Images have been identified and show the dataset from it matched. The Results are accurate and shows efficiency. In this firstly binary silhouette of a walking person is detected from each frame of an image. Then secondly, the features from each frame are extracted using the image processing operation. In the end SVM, K-MEANS and LDA are used for training and testing purpose. Every experiment and test is done on CASIA database. The results in this paper are better and improved from previous results by using SVM , K MEANS. DOI: 10.17762/ijritcc2321-8169.15067

Finger Vein Recognition Based on PCA Feature using Artificial Neural Network

Personal recognition technology is developing rapidly as a security system. Traditional methods such as authentication key; password: card is not secure enough, because they could be stolen or easily forget. Biometrics has been applied to a wide range of systems. According to various researchers, vein biometrics was a good technique from other biometric authentication system used, such as fingerprints, hand geometry, voice, etc. of the DNA. Root Authentication systems can be designed in different ways. All methods include the matching stage. A neural network is an effective way of matching Personal identification authentication system. The finger vein pattern is unique biometric identity of the human beings. The finger vein recognition is a popular biometric technique which is used for authentication purposes in various applications. In the propose work an algorithm is proposed to find the accuracy, FRR and FAR of finger vein recognition. The performances of PCA, threshold segmentation, pre-processing and testing & training techniques has been validate and compared with each other in order to determine the most accurate results in terms of finger vein recognition

Finger Vein Recognition Based on a Personalized Best Bit Map

Finger vein patterns have recently been recognized as an effective biometric identifier. In this paper, we propose a finger vein recognition method based on a personalized best bit map (PBBM). Our method is rooted in a local binary pattern based method and then inclined to use the best bits only for matching. We first present the concept of PBBM and the generating algorithm. Then we propose the finger vein recognition framework, which consists of preprocessing, feature extraction, and matching. Finally, we design extensive experiments to evaluate the effectiveness of our proposal. Experimental results show that PBBM achieves not only better performance, but also high robustness and reliability. In addition, PBBM can be used as a general framework for binary pattern based recognition