Application of Machine Learning in Fingerprint Image Enhancement and Recognition: A Review

Biometric characteristics helps to recognize an individual among others. Each individual has a unique biometric feature. So, an automated system is designed to recognize an individual. In today’s growing AI development, biometric recognition is applied in many security systems. One of oldest and widely used authentic biometric methodology is fingerprint recognition. Many fingerprint recognition algorithms are designed and developed in order to reduce error rate and to improve accuracy. In this paper, a comprehensive review is presented on various techniques used for fingerprint recognition system along with their performance and their limitations. The purpose of this paper is to review various recent work on the fingerprint recognition system, to explain step by step the steps for recognizing fingerprints, and to provide summaries of the fingerprint databases with functionality

Criminal Face Recognition Using GAN

Forensics and security at present often use low technological resources. Security measures often fail to update with the upcoming technology. This project is based on implementing an automatic face recognition of criminals or specific targets using machine-learning approach. Given a set of features to a Generative Adversarial Network(GAN), the algorithm generates an image of the target with the specified feature set. The input to the machine can either be a given set of features or a set of portraits varying from frontals to side profiles from which these features can be extracted. The accuracy of the system is directly proportional to the number of epochs trained in the network. The generated output image can vary from primitive, low resolution images to high quality images where features are more recognizable. This is then compared with a predefined database of existing people. Thus, the target can immediately be recognized with the generation of an artificial image with the given biometric feature set, which will be again compared by a discriminator network to check the true identity of the target.

Fingerprinting of Relational Databases for Stopping the Data Theft

The currently-emerging technology demands sharing of data using various channels via the Internet, disks, etc. Some recipients of this data can also become traitors by leaking the important data. As a result, the data breaches due to data leakage are also increasing. These breaches include unauthorized distribution, duplication, and sale. The identification of a guilty agent responsible for such breaches is important for: (i) punishing the culprit; and (ii) preventing the innocent user from accusation and punishment. Fingerprinting techniques provide a mechanism for classifying the guilty agent from multiple recipients and also help to prevent the innocent user from being accused of the data breach. To those ends, in this paper, a novel fingerprinting framework has been proposed using a biometric feature as a digital mark (signature). The use of machine learning has also been introduced to make this framework intelligent, particularly for preserving the data usability. An attack channel has also been used to evaluate the robustness of the proposed scheme. The experimental study was also conducted to demonstrate that the proposed technique is robust against several malicious attacks, such as subset selection attacks, mix and match attacks, collusion attacks, deletion attacks, insertion attacks, and alteration attacks.

Image-Based Somatotype as a Biometric Trait for Non-Collaborative Person Recognition at a Distance and On-The-Move

It has recently been shown in Re-Identification (Re-ID) work that full-body images of people reveal their somatotype, even after change in apparel. A significant advantage of this biometric trait is that it can easily be captured, even at a distance, as a full-body image of a person, taken by a standard 2D camera. In this work, full-body image-based somatotype is investigated as a novel soft biometric feature for person recognition at a distance and on-the-move. The two common scenarios of (i) identification and (ii) verification are both studied and evaluated. To this end, two different deep networks have been recruited, one for the identification and one for the verification scenario. Experiments have been conducted on popular, publicly available datasets and the results indicate that somatotype can indeed be a valuable biometric trait for identity recognition at a distance and on-the-move (and hence also suitable for non-collaborative individuals) due to the ease of obtaining the required images. This soft biometric trait can be especially useful under a wider biometric fusion scheme.

Integrated Framework for Anonymous Biometric Key Based Identity Management System

Implementation of measures to ensure security of transactions while ensuring privacy of user credentials is an area of challenge in digital transactions over a network. Integration of biometric pattern matching into an identity management system (IMS) enhances security of transactions and improves ease of use. Privacy of users in a biometric based system is improved by using keys generated directly from feature sets instead of conventional stored templates. This paper proposes a framework for integrating biometric key based authentication into an IMS.The generated keys need to be long, reproducible with high integrity and need to possess sufficient entropy. Generation of keys directly from feature traits poses a challenge due to intra and inter user variations inherent to biometric data. A novel methodology for generating and integrating crytpo keys into an identity management system is proposed. The keys have been extracted from iris trait. 300 bits keys have been extracted from iris datasets. The results are promising and can be extended to multi-modal biometric feature sets.