Vinayak Ashok Bharadi

Bio: Vinayak Ashok Bharadi is an academic researcher from Academy of Management. The author has contributed to research in topics: Feature vector & Biometrics. The author has an hindex of 14, co-authored 90 publications receiving 638 citations. Previous affiliations of Vinayak Ashok Bharadi include Thakur College of Engineering and Technology & University of Mumbai.

Off-Line Signature Recognition Systems

Abstract: Handwritten signature is one of the most widely used biometric traits for authentication of person as well as document. In this paper we discuss issues regarding off-line signature recognitions. We review existing techniques, their performance and method for feature extraction. We discuss a system designed using cluster based global features which is a multi algorithmic offline signature recognition system.

Fingerprint classification using a deep convolutional neural network

Abstract: Biometric systems detect authenticity based on users' distinct physiological or behavioral characteristics for purposes of identification and access control. These pattern recognition systems are difficult to bypass when compared to traditional token or password based systems. This paper is proposing a new deep learning architecture for fingerprint recognition. The proposed architecture comprises of a pre-processing stage for extracting texture features from fingerprints, and this stage is performed by using histogram equalization, Gabor enhancement and fingerprint thinning. The pre-processed fingerprints are input into a Deep Convolutional Neural Network classifier. The proposed approach has achieved 98.21% classification accuracy with 0.9 loss. The obtained accuracy is significantly higher than previously reported results on the same dataset, 77%.

Gabor Filter Based Feature Vector for Dynamic Signature Recognition

Abstract: Dynamic signature recognition is one of the commonly used biometric traits. In this paper we propose use of Gabor filters based feature for verification of dynamic signature. We incorporate the timing information available in the signature along with the Gabor filter response to generate the feature vector. Gabor filters have been widely used for image, texture analysis. Here we present analysis for the Gabor filter based feature vector of a dynamic signature.

Image Classification Using Deep Learning

Abstract: Image Classification nowadays is used to narrow the gap between the computer vision and human vision so that the images can be recognized by machines in the same way as we humans do. It deals with assigning the appropriate class for the given image. We therefore propose a system named Image Classification using Deep Learning that classifies the given images using Classifiers like Neural Network. This system will be developed to measure the accuracy of classifying images on GPU (NVIDIA) and CPU. The system will be designed using Python as a Programming Language and Tensorflow for creating neural

Handbook Of Biometrics

Abstract: Thank you very much for reading handbook of biometrics. Maybe you have knowledge that, people have look numerous times for their favorite books like this handbook of biometrics, but end up in malicious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they are facing with some harmful virus inside their desktop computer. handbook of biometrics is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the handbook of biometrics is universally compatible with any devices to read.

Security and Accuracy of Fingerprint-Based Biometrics: A Review

Abstract: Biometric systems are increasingly replacing traditional password- and token-based authentication systems. Security and recognition accuracy are the two most important aspects to consider in designing a biometric system. In this paper, a comprehensive review is presented to shed light on the latest developments in the study of fingerprint-based biometrics covering these two aspects with a view to improving system security and recognition accuracy. Based on a thorough analysis and discussion, limitations of existing research work are outlined and suggestions for future work are provided. It is shown in the paper that researchers continue to face challenges in tackling the two most critical attacks to biometric systems, namely, attacks to the user interface and template databases. How to design proper countermeasures to thwart these attacks, thereby providing strong security and yet at the same time maintaining high recognition accuracy, is a hot research topic currently, as well as in the foreseeable future. Moreover, recognition accuracy under non-ideal conditions is more likely to be unsatisfactory and thus needs particular attention in biometric system design. Related challenges and current research trends are also outlined in this paper.

Exploring Touch-Screen Biometrics for User Identification on Smart Phones

Abstract: The use of mobile smart devices for storing sensitive information and accessing online services is increasing. At the same time, methods for authenticating users into their devices and online services that are not only secure, but also privacy and user-friendly are needed. In this paper, we present our initial explorations of the use of lock pattern dynamics as a secure and user-friendly two-factor authentication method. We developed an application for the Android mobile platform to collect data on the way individuals draw lock patterns on a touchscreen. Using a Random Forest machine learning classifier this method achieves an average Equal Error Rate (EER) of approximately 10.39%, meaning that lock patterns biometrics can be used for identifying users towards their device, but could also pose a threat to privacy if the users’ biometric information is handled outside their control.