Biometric Authentication of Remote Fingerprint Live Scan Using Artificial Neural Network with Back Propagation Algorithm and Possibility for Wider Security Applications (Published)
This study is aim to experiments the development of an automated foolproof university library system that integrates fingerprint technique with fingerprint-based Personally Identified Number (PIN)/password architecture for enhanced registration and login security. The development environment for creating the electronic library application for universities as RESTful Web Service is Jersey Framework. This framework implements JAS-RX 2.0 API, which is a de facto specification for developing a RESTful Web Service-based software system. Other necessary programming technologies employed in the research work are JDK, Apache Tomcat and Eclipse, which were set up prior to setting up the Jersey Framework as the development environment. The study is therefore summarized by generating hash digital values of perfectly matched reference shape signatures formed from the extraction of global minutiae features, comparing and further matching each hash value with its corresponding highly encrypted password equivalence for unique establishment of a person’s identity, minimal mean-square errors and unnecessary ambiguity introduced through false positives, as an extended security enhancement measure in biometric systems. , the study investigates the algorithm for generating templates for matching minutiae [10] together with the algorithm for generating reference axis [11], which infers that for a pair of minutiae (pn , q0) to match, there exists a reference point that corresponds between the two fingerprint images. The experimental result shows that the Sample fingerprint images were captured using a biometric scanner, which was integrated with the help of JAVA libraries, and stored in a database as raw image files..
Keywords: Algorithm, Artificial Neural Network, Biometric, live scan, rest architecture
IRIS IDENTIFICATION SYSTEM BASED ON RLM TEXTURE FEATURES (Published)
Among biometric identification technologies, iris recognition has attracted lots of attention because of uniqueness and long term stability. In this paper, a new iris recognition system based on texture has been proposed and used for identification of person. Texture features such as Run Length Matrix features (RLM) will be used for feature extraction. Inner and outer boundary will detect. Then iris region will divide into blocks and the importance degree of each block will assign as weights according to sigmoid function. Sixteen RLM features will be computed (eight features in each 0 and 90 directions). To evaluate the performance of proposed method, it applied to identify iris image from MMU ver.1 data set. Experimental results on 88 classes show that the new method give good recognition rates (99.3%) with smallest features vector as compared with other methods
Keywords: Biometric, Iris Recognition, Local Enhancement, RLM, Texture Feature