Necla Özkaya

An Intelligent Automatic Fingerprint Recognition System Design

This work presents an intelligent automatic fingerprint identification and verification system based on artificial neural networks. In this work, the design processes of the system have been presented step by step. In order to make the system automatic, software was developed for fingerprint identification and verification processes. 100 fingerprint images were used to test and evaluate the system. The results have shown that the task was achieved with high accuracy

Intelligent face border generation system from fingerprints

Although biometrics is a deeply studied field, relationships among biometric features have not been studied in the field so far. In this study, we have analysed the existence of any relationship among biometric features. Also we have tried to generate the face border of a person using only fingerprint biometric feature of the same person without any information about his or her face. Consequently, for generating face borders from only fingerprints, we have designed and introduced a novel intelligent system based on artificial neural networks having the absolute percent errors between 1.49 and 9.86. Experimental results have shown that fingerprints and face borders have relations among each other closely. In addition, it is demonstrated that generating face borders from fingerprints without knowing any information about faces is possible. Although this study has been the first step of the research, the results are very encouraging and promising for new challenges.

Artificial neural network based automatic face parts prediction system from only fingerprints

Biometrics is a deeply studied and highly developed technology. While biometric systems have been used primarily in limited applications requiring high security tasks like criminal identification and police work, more recently they have been receiving increasing demand for person recognition applications. In spite of all developments in biometrics, there is no study investigating relationships between biometric features in the literature. This study presents a novel intelligent approach analysing the existence of any relationship among fingerprints and face parts. Proposed approach is based on artificial neural networks. Developed system generates the stationary face parts of a person including eyebrows, eyes and nose from only one fingerprint image of the same person without knowing any information about his or her face with the errors among 1.4 % and 4.8 %. The satisfactory results have indicated that there are close realitionships among fingerprints and faces. Improving of the proposed system is still sustained for the purpose of analysing and modelling of this relationship for the future developments in biometrics and security applications.

Translating the fingerprints to the faces: A new approach

Although many approaches and algorithms about biometric recognition techniques have been developed and proposed in the literature; relationships about biometrics have not been studied in the field so far. In this study, we have analysised the existence of any relationship between biometric features and we have tried to obtain a biometric feature of a person from another biometric feature of the same person. Consequently, we have designed and introduced a new and intelligent system using a novel approach based on artificial neural networks for generating faces from fingerprints with % 5.68 mean absolute percent errors. Experimental results have demonstrated that it is possible to generate faces from fingerprints without knowing any information about faces. In addition it is shown that fingerprints and faces are related to each other closely. In spite of the proposed system is initial study and it is still under development, the results are very encouraging and promising.

Intelligent face mask prediction system

Biometric based person identification systems are used to provide alternative solutions for security. Although many approaches and algorithms for biometric recognition techniques have been developed and proposed in the literature, relationships among biometric features have not been studied in the field so far. In this study, we have analysed the existence of any relationship between biometric features and we have tried to obtain a biometric feature of a person from another biometric feature of the same person. Consequently, we have designed and introduced a new and intelligent system using a novel approach based on artificial neural networks for generating face masks including eyes, nose and mouth from fingerprints with 0.75-3.60 absolute percent errors. Experimental results have demonstrated that it is possible to generate face masks from fingerprints without knowing any information about faces. In addition it is shown that fingerprints and faces are related to each other closely. In spite of the proposed system is initial study and it is still under development, the results are very encouraging and promising. Also proposed work is very important from view of the point that it is a new research area in biometrics.

Discriminative common vector based finger knuckle recognition

Graphical abstractDisplay Omitted This paper presents an effective finger knuckle print recognition approach.Discriminative common vector based method is used to achieve the recognition task.In test, the most representative public database is used. Recognition rate: 100%.In test, an established non-uniform database is also used. Recognition rate: 100%.It is attained to the aimed goal with 100% RR, and approximately 0 EER. The main issue in personal authentication systems for military, security, industrial and social applications is accuracy. This paper presents a finger knuckle print (FKP) recognition approach to identity authentication. It applies a discriminative common vectors (DCV) based method to obtain the unique feature vectors, called discriminative common vectors, and the Euclidean distance as matching strategy to achieve the identification and verification tasks. The recognition process can be divided into the following phases: capturing the image; pre-processing; extracting the discriminative common vectors; matching and, finally, making a decision. In order to test and evaluate the proposed approach both the most representative FKP public databases and an established non-uniform FKP database were used. Experiments with these databases confirm that the DCV-based FKP recognition method achieves the authentication tasks effectively. The results showed the performance of the system in terms of the recognition rate had 100% accuracy for both training data and unseen test data.

Artificial Neural Network Based Automatic Face Model Generation System from Only One Fingerprint

Biometrics technology has received increasingly more attention during the last three decades. Since the performance of biometric systems has reached a satisfactory level for applications, a number of biometric features have been deeply studied, tested and successfully deployed in applications. Relationships among biometric features have not been studied so far. This study focuses on analysing the existence of any relationships among fingerprints and faces. For doing that an intelligent system based on artificial neural networks for generating face models including eyes, nose, mouth, earsandface borderfrom only one fingerprint with the errors among 2.0-12.9 % was developed. Experimental results have shown that there are close realitionships among fingerprints and faces and it is possible to generate faces from only one fingerprint image without knowing any information about faces. Although the proposed system is an initial study and it is still under development, the results are very encouraging and promising for the future developments and applications.

Metacarpophalangeal joint patterns based personal identification system

A new biometric identifier: whole MJP pattern is introduced.An effective, fast and robust MJP based biometric system is developed and presented.Discriminative common vector based method is firstly applied to obtain the feature sets of MJPs. This paper introduces a novel approach for identity authentication system based on metacarpophalangeal joint patterns (MJPs). A discriminative common vector (DCV) based method is utilized for feature selection. In the literature, there is no study using whole MJP for identity authentication, exceptionally a work (Ferrer et al., 2005) using the hand knuckle pattern which is some part of the MJP draws the attention as a similar study. The originality of this approach is that: whole MJP is firstly used as a biometric identifier and DCV method is firstly applied for extracting the feature set of MJP. The developed system performs some basic tasks like image acquisition, image pre-processing, feature extraction, matching, and performance evaluation. The feasibility and effectiveness of this approach is rigorously evaluated using the k-fold cross validation technique on two different databases: a publicly available database and a specially established database. The experimental results indicate that the MJPs are very distinctive biometric identifiers and can be securely used in biometric identification and verification systems, DCV method is successfully employed for obtaining the feature set of MJPs and proposed MJP based authentication approach is very successful according to state of the art techniques with a recognition rate of between 95.33% and 100.00%.

An Intelligent and Automatic Face Shape Prediction System From Fingerprints

Abstract This paper presents an intelligent system for generating face shapes from only fingerprints without knowing any information about faces. The proposed system based on artificial neural network has got a number of modules including two biometric data acquisition modules, two feature extraction modules, an artificial neural network module, a face re-construction module and a test &evaluation module. Experimental results have shown that the faces can be successfully generated from only fmgerprints. Although the proposed system is an initial study, the performance of the system is very promising for the future developments.

An intelligent automatic face contour prediction system

Even if biometric features have been deeply studied, tested and successfully applied to many applications, there is no study in achieving a biometric feature one from another. This study presents a novel intelligent approach analysing the existence of any relationship among fingerprints and faces. The approach is based on artificial neural networks to generate face contour of a person from only his/her fingerprint. Experimental results have shown that there are close relationships among the features of fingerprints and faces. It is possible to generate face contours from fingerprint images without knowing any information about faces. Although the proposed system is initial study and it is still under development, the performance of the system is very encouraging and promising for the future developments and applications.