Use fingerprint technology in developing country security

The security threats to the personal information of Internet users raise the need for improved security measures and authentication mechanisms of username and password so that intruders can be discouraged. Another problem faced by modern-day Internet users is that they are required to maintain a long list of passwords for their web accounts maybe 15-20 accounts. Therefore, this raises the need to introduce a better and more reliable authentication mechanism which is not dependent on a series of characters, but rather on a technology that is unique and only possessed by the individual. However, there are some secure services that revolve around the distributed sharing of data, and do not provide any log-in facilities for any website or web account with high level of security, accessibility and usability. This paper presents an answer to all these problems in a single solution, named FingerID, by focusing the theoretical background of the subject and an in-depth review of the literature available on various aspects of security provisions used in the industry for distributed systems. During the course, the objective is to identify clear criteria which would enable robust comparison between FingerID and various similar applications available in the market.

This paper deals with the fingerprint classification and recognition system, which consists of extracting and matching of minutiae from the input image. The necessity for security in fields such as improving airport security, strengthening the national borders, in travel documents, in preventing ID theft has brought the need to develop an able and efficient method for correct classification of personnel authentication. Fingerprints are the first biometric science used widely for the validation and verifications of entry into specific task, which is more reliable, efficient and accurate. Despite finger print recognition being reliable it has disadvantages such as very low recognition rate, low accuracy rate, total time of recovery and data insufficiency. To address above problem a novel data mining technique, neuro nearest neighbour based fingerprint classification and recognition, is introduced which boosts the classification rate. The proposed method consists of different stages such as image enhancement, line detector based feature extraction, neural network classification using Learning vector quantization and Back propagation networks. The proposed system is trained and tested on Fingerprint Database obtained from the University of Bologna Italy, which consists of 900 samples. The exact image is recognized precisely from the classified database rather than the original set of database using Crisp K-nearest neighbour algorithm that increases recognition accuracy and reduction in time

A multimodal biometrics face and fingerprint recognition system is a computer application for automatically identifying or verifying a person from face by using cameras and fingerprint by using sensors or fingerprint readers or fingerprint scanners. Proposed paper uses Face and Fingerprint recognition technique for verification in ATM systems. There are two types. The first one is verification. Compare the two faces and fingerprint images and decide whether the user (current user image) is an genuine user or imposter. These are decision level. Second one is identification this is where the system compares the given input image to all other images in the database and gives a ranked list of matches. Multimodal biometrics verification system that verifies the presence of a user is genuine or not. Two modalities are currently used—face and fingerprint—but our theory can be readily extended to include more modalities. We show that verification imposes additional requirements on multimodal fusion when compared to conventional verification systems. We also argue that the usual performance metrics of false accept and false reject rates are insufficient yardsticks for continuous verification and propose new metrics against which we benchmark our system.

The most common approach for fingerprint analysis is using minutiae that identifies corresponding features and evaluates the resemblance between two fingerprint impressions. Although many minutiae point pattern matching algorithms have been proposed, reliable automatic fingerprint verification remains as a challenging problem. Finger print recognition can be done effectively using texture classification approach. Important aspect here is appropriate selection of features that recognize the finger print. We propose an effective combination of features for multi-scale and multi-directional recognition of fingerprints. The features include standard deviation, kurtosis, and skewness . We apply the method by analyzing the finger prints with discrete wavelet transform (DWT) . We used Canberra distance metric for similarity comparison between the texture classes. We trained 30 images and obtained an overall performance up to 96%.

Biometric authentication plays a key role in smart environments, with finger print recognition being one of the most common methods. Fingerprint readers scan and store fingerprint data, comparing it during login attempts to verify identity. While effective, these systems are vulnerable to spoofing using materials like wax, clay, or gelatine. Criminals sometimes exploit this technology to access personal information. Two main algorithms, minutiae matching and pattern matching, are used for recognition. Minutiae matching compares unique points on fingerprints, while pattern matching analyzes surface thickness, curvature, and ridge patterns. However, injuries to the fingercan make identification challenging. Advances now integrate sensors to detect pulse and sweat glands, enhancing security. Fingerprint recognition is widely employed in legal systems for identity management, but the technology relies heavily on intact surface patterns for accurate matching.

The purpose of this research is to build electronic voting machine to increase transparency in election. This system uses microcontroller as the central part which registers the vote of the individual, the vote will be cost by pushing the button where for the identification of voter, figure print scanning module is used. Human finger prints are rich in minutia which are used in finger prints verification. This paper focuses on finger prints system of identification, which actually is minutia matching (MM), which is widely used in fingerprint algorithms.The technique include extraction from fingerprints the minutia points (MP). The technique is to match two fingerprint images by performing fingerprint matching technique. The main steps used for recognition are enhancement of image, segmentation of image, extraction of minutia and its matching

Since the emergence of ChatGPT in 2022, generative artificial intelligence has brought rapid changes across industries and daily life. Because artificial intelligence model performance depends on the quality and scale of training data, companies are building large-scale training datasets by utilizing publicly available personal information and personal information of users they already possess. However, legal uncertainty persists as it remains unclear whether the processing of artificial intelligence training data containing personal information is lawful under personal information protection laws. Accordingly, this paper examines the concept, characteristics, and privacy risks of artificial intelligence training, and reviews interpretations regarding the processing of artificial intelligence training data, focusing on publicly available personal information and personal information of existing users, while analyzing their limitations. Ultimately, to achieve a balance between personal information protection and artificial intelligence industry development, it is urgent to clarify the lawful basis for processing artificial intelligence training data containing personal information and establish legal standards that can guarantee the rights of data subjects. Through this, it will be possible to resolve legal uncertainty for businesses, protect the right to self-determination of personal information, and promote trust-based development of the artificial intelligence industry.

Fingerprint recognition using a joint transform correlator (JTC) is the most well-known technology among optical fingerprint recognition methods. The JTC method optically compares the reference fingerprint image with the sample fingerprint image then examines match or non-match by acquiring a correlation peak. In contrast to the JTC method, this paper presents a new method to examine fingerprint recognition by producing a computer generated hologram (CGH) of those two fingerprint images and directly comparing them. As a result, we present some parameters to show that fingerprint recognition capability of the CGH direct comparison method is superior to that of the JTC method.

With the continuous development of the Internet and communication technologies, network communication provides convenience but also brings security problems such as exposure of users' personal privacy information and theft, private tampering, and forgery of false information. Modern cryptography technology is an important safeguard against message eavesdropping and tampering, while the rapidly developing anonymous communication technology in this century makes it difficult for attackers to infer user's personal information and communication relationships. In response to the potential threats of traditional centralized systems such as central nodes being vulnerable to attacks and data storage being tampered with, this paper proposes a blockchain anonymous communication algorithm KDAC based on key derivation, which takes advantage of the decentralization, data immutability, consensus mechanism and anonymity of blockchain and combines the ECC cryptographic derivation algorithm and anonymous communication technology to realize the key-at-a-time, one-address-at-a-time The key derivation scheme ensures the message integrity and tamper-evident while effectively hiding the identity information of both communication parties. In addition, this paper also optimizes the blockchain anonymous communication system with key derivation. Users only need the initial key of blockchain nodes to join the network for communication, and the information transmission is difficult to trace based on the blockchain network, which can effectively guarantee communication security and anonymity. The experimental results show that the efficiency of the derived key algorithm is roughly in the same order of magnitude as that of the 256-bit AES symmetric encryption algorithm, which can play a better role in practical applications. On the other hand, the derived key generated based on the algorithm has complete randomness in association verification, and it is impossible to reverse the initial parameters, which can well guarantee the anonymity of user identity.

This report provides an overview of Namibia’s biometric identification (ID) system. It centres on the national ID system that has evolved from paper based documentation dating from pre-1990 to increasingly biometric inclusive documentation that incorporates fingerprint and facial recognition technologies. The introduction of the of biometric ID, and its gradual rollout within the refugee community, specifically, is recognised as necessary to upscale service provision for essential services such as providing access to social grants, while also facilitating cross-border movement, and is among the many digital activities needed to transform Namibia into a knowledge-based economy. This research assesses the current state of the biometric ID system and existing legislative framework, while also comparing operational implementation against established rule of law, rights-based, and risk-based tests. This report evaluates the strengths and vulnerabilities in the system’s design and governance; it examines the system’s effectiveness, and balances security considerations with citizens’ rights to privacy, inclusion, and non-discrimination.

Information should be shared effectively and securely to protect users' personal identifiable information. Using a chatbot, or a computer application to share information presents a likelihood of identity theft if not properly secured. In communicating with chatbots, the user’s information is held in the chatbot application database. The availability of user information on this application may give opportunity to attackers to hijack personal user information to perpetrate crime. Unfortunately, current chatbot applications do not have a mechanism to delete or remove personal identifiable information of users. Some chatbots accept users' personal identifiable information with no recourse to the user on what happens after the chat session. To prevent a third party from trading, selling, or hacking the data, this research designed a decision system to be added to the chatbot application that will enable the user to consciously decide on how their personal identifiable information should be treated or kept. The chatbot was implemented in Hypertext Markup Language (HTML), using PHP and MySQL for standard web development. Also, a PHP server was installed for the system to relate properly with the database server in fetching and storing the data. The system was tested using likelihoods that may lead to the success of the program. This was done repeatedly with different test data. The new system provides users consent on whether their personal information is left on the chatbot app or removed completely.

Biometric technologies try to automatically recognize individuals by considering the physiological and behavioral characteristics of individuals. Although the methods used here are very diverse, the personal qualities used also vary. Facial features, finger and vein prints, iris, retina, ear, hand, and finger recognition are only some of the physiological features. It may be preferred to use one or more of these personal features to reduce the margin of error that may arise depending on the security level in the applications used. Biometric recognition systems have varying requirements in security systems applications. Fingerprint and iris recognition work well in applications that require high security levels, while applications that require low security levels are not suitable due to privacy concerns. On the other hand, identification from hand images is more accepted based on the idea that it does not have a very high distinctiveness. But it is sufficient for medium security applications. Apart from these, palm images have many advantages such as reliability, stability, user-friendliness, non-intrusiveness, and flexible use. In this study, it is aimed to identify people, determine their ages, and determine their gender by using both upper surface and inner surface images of right-left hand data of hand shape. For this purpose, images of both the inner surface of the hand (10) and the outer surface of the hand (10) of 100 different people were collected. This was done separately for the right and left hands, and a total of 3955 images were obtained. The features of these images were extracted using the Median Robust Extended Local Binary Model (MRELBP). Images are classified for person, age and gender. The results were 91.4%, 85.9% and 92.6%, respectively.

<p><em>The aliveness of a person can be related with the heart beat rhythm. So the heart beat rhythm can be signalized into an ECG. Nowadays for the biometric authentication purpose we normally use the finger print, face &iris recognition etc. Among that ECG is more against forgeries and it is advanced technique than finger print &face recognition. This paper describes different methods used in identifying a person using Electrocardiogram signals. The same signal used for the classification of emotions also. User identification is the common method for identify a user in the business field and other organizations.  User ID is one of the most common authentication system used within computing systems, networks, applications and over the Internet. Commonly used authentications are fingerprint, face recognition, iris recognition & RFID etc. Among ECG is universal, continuous& difficult to falsify. So the user identification using ECG is the new trend in biometric authentication. ECG is a periodical signal creates P,QRS,T through depolarization of atrium, repolarization &depolarization of ventricle. ECG is a bio signal & is unique because the variation in amplitude of R wave and the time duration of each wave is different for each individual. There are many different types of </em><a href="https://www.verywellmind.com/what-are-emotions-2795178"><em>emotions</em></a><em> that have an influence on how we live on earth and how we behave to others. The emotions are identified were happiness, sadness, disgust, fear, surprise, and anger. This paper describes the different method used to identify the person &classifies the emotion using ECG.</em></p>

Individual recognition systems with high-speed and high-accuracy have been recently demanded in the automatic logging into a PC, the immigration at the airport, the access control and diligence & indolence management in an office, and so on. Biometric authentication is now being regarded as the most valid method because of the receptivity, individuality and invariability of biometric identifiers. Various types of the individual recognition systems based on biometrics have been studied and partially realized. Fingerprints, faces, hand geometry, irises, vein patterns, gait, signatures, etc., are known as biometric identifiers. In particular, the fingerprint recognition system has been widely used because of its high reliability and reasonable price (Maltoni et al., 2003a; Jain et al., 2010). The fingerprint recognition methods can be classified into the following three types: (i) the minutiae-based, (ii) the frequency-based and (iii) the image-based methods. The minutiae-based method is mainly being used in the practical fingerprint recognition system. For example, a memetic fingerprint matching algorithm has been recently proposed to identify the optimal or near optimal global matching between two minutiae sets (Sheng et al., 2007). A matching technique for fingerprint recognition using the Minutia CylinderCode (MCC), which is based on 3D data structures built from minutiae distances and angles, has also been made a proposal (Cappelli et al., 2010) to exclude the drawbacks in the fingerprint authentication using local minutiae structures. Moreover, a fingerprint verification using spectral minutiae representations has been suggested to overcome translation, rotation and scaling which are the drawbacks of minutiae-based algorithms (Xu et al., 2009a, 2009b). The frequency-based method, such as the frequency analysis method, is also being used in the practical fingerprint recognition system in order to secretly hide the original fingerprint information (Takeuchi et al., 2007). Recently, a fingerprint recognition method based on melfrequency cepstral coefficients and polynomial shape coefficients has been proposed because of the robustness to noise and the insensivity to translation (Hashad et al., 2010). The image-based method has been being studied to improve the accuracy in the fingerprint recognition. For example, an enhanced image-based algorithm for fingerprint verification based on invariant moment features has been recently proposed to improve matching accuracy and processing speed (Yang & Park, 2008a, 2008b). A novel image-based fingerprint matcher based on the minutiae alignment has also been made a proposal to improve the verification performance (Nanni & Lumini, 2009).

In the context of third-party social apps, the problem of interdependency of privacy refers to users making app adoption decisions which cause the collection and utilization of personal information of users’ friends. In contrast, users’ friends have typically little or no direct influence over these decision-making processes. We conduct a conjoint analysis study with two treatment conditions which vary the app data collection context (i.e., to which degree the functionality of the app makes it necessary for the app developer to collect friends’ information). Analyzing the data, we are able to quantify the monetary value which app users place on their friends’ and their own personal information in each context. Combining these valuations with the responses to a comprehensive survey, we apply structural equation modeling (SEM) analysis to investigate the roles of privacy concern, its antecedents, as well as app data collection context to work towards a model of interdependent privacy for the scenario of third-party social app adoption. We find that individuals’ past experiences regarding privacy invasions are negatively associated with their trust for third-party social apps’ proper handling of their personal information, which in turn influences their concerns for their own privacy associated with third-party social apps. In addition, positive effects of users’ privacy knowledge on concerns for their own privacy and concerns for friends’ privacy regarding app adoption are partially supported. These privacy concerns are further found to affect how users value their own and their friends’ personal information. However, we are unable to support an association between users’ online social capital and their concerns for friends’ privacy. Nor do we have enough evidence to show that treatment conditions moderate the association between the concern for friends’ personal information and the value of such information in app adoption contexts.