Efficient Biometric Identification Research Articles

Utilizing Convolutional Neural Networks for Fingerprint-Based Attendance Monitoring

The traditional method of taking attendance using paper sheets is prone to errors like impersonation, loss, or theft. To solve this issue, automatic attendance systems utilizing identification technology such as barcode badges, electronic tags, touch screens, magnetic stripe cards, and biometrics have been implemented. Biometric technology uses physiological or behavioral characteristics for identification purposes, but traditional biometric systems have limitations such as vulnerability to damage or alteration over time, and variations in occlusions, poses, facial expressions, and illumination can affect face recognition accuracy. Fingerprint identification relies on the distinctiveness of fingerprints and involves comparing two impressions of the friction ridges on human fingers or toes to determine if they belong to the same individual. There are five primary categories of fingerprints: arch, tented arch, left loop, right loop, and whorl. Various algorithms have been developed to recognize fingerprints using minutiaebased matching, which involves identifying key features like ridge ending and bifurcation. Deep learning algorithms, particularly convolutional neural networks, have been successful in improving identification accuracy by extracting features automatically from fingerprint images. In recent times, securing personal data has become increasingly important, and the Convolutional Neural Network (CNN) identification system is recommended for improving accuracy and performance. This paper proposes a fingerprint identification system that combines three models: CNN, Softmax, and Random Forest (RF) classifiers. The conventional system uses K-means and DBSCAN algorithms to separate the foreground and background regions and extracts features using CNNs and dropout approach. The Softmax acts as a recognizer. The proposed algorithm is evaluated on a public database and shows promising results, providing an accurate and efficient biometric identification system.

Efficient Biometric Identification on the Cloud With Privacy Preservation Guarantee

Benefited from its reliability and convenience, biometric identification has become one of the most popular authentication technologies. Due to the sensitivity of biometric data, various privacy-preserving biometric identification protocols have been proposed. However, the low computational efficiency or the security vulnerabilities of these protocols limit their wide deployment in practice. To further improve the efficiency and enhance the security, in this paper, we propose two new privacy-preserving biometric identification outsourcing protocols. One mainly utilizes the efficient Householder transformation and permutation technique to realize the high-efficiency intention under the known candidate attack model. The other initializes a novel random split technique and combines it with the invertible linear transformation to achieve a higher security requirement under the known-plaintext attack model. Also, we argue the security of our proposed two protocols with a strict theoretical analysis and, by comparing them with the prior existing works, comprehensively evaluate their efficiency.

Performance Evaluation of New Feature based on Ordinal Pattern Analysis for Iris Biometric Recognition

The Iris recognition technique is currently the most efficient biometric identification system and is a common system on the practical front. Though most of the commercial systems use the patented Daugman’s algorithm, which mainly uses wavelet-based features, research is still active in identifying novel features that can provide personal identification. Here the first novel proposal of using ordinal pattern measure based on nonlinear time series analysis is put forth to characterize the unique pattern of the iris of individuals and thereby perform personal identification. Dispersion Entropy is a nonlinear time-series analysis method highly efficient in the characterization of the complexity of any data series with proven effectiveness in the characterization of model system dynamics as well as real-world data series. The results show that dispersion entropy can be used to identify iris images of specific individuals. The efficiency of this method is evaluated by computing correlation and RMSE between dispersion entropy values of normalized iris image rubber sheet data. The experimental results on the popular IRIS database- CASIA v1- demonstrate that the proposed method can effectively perform differential identification of iris images from different individuals. The results specifically indicate that the density of information along the angular direction of iris images which falls along the rows of rubber sheet data. This can be efficiently utilized with the method or ordinal pattern characterization and proves to be having promising potential for being incorporated into biometrics personal identification systems.

Cancelable Permutation-Based Indexing for Secure and Efficient Biometric Identification

Cancelable biometric schemes have been widely studied to protect templates in biometric authentication over networks. These schemes transform biometric features and perform pattern matching without restoring the original features. Although they strongly prevent the leakage of the original features, the response time can be very long in a large-scale biometric identification system. Most of the existing indexing schemes cannot be used to speed up the biometric identification system over networks since a biometric index leaks some information about the original feature. Secure and efficient indexing is a major challenge in large-scale biometric identification over networks. In this paper, we propose a novel indexing scheme that is promising with regard to both security and efficiency. The proposed indexing scheme transforms a permutation-based index, which is the state-of-the-art index in the field of similarity search, and performs a query search without recovering the original index. We also propose a method to artificially generate biometric features necessary to generate an index (which are called “pivots”) based on GANs (Generative Adversarial Networks). We prove that the transformed index leaks no information about the original index and the original biometric feature (i.e., perfect secrecy), and comprehensively show that the proposed indexing scheme has the irreversibility, unlinkability, and revocability. We then demonstrate that the proposed indexing scheme significantly outperforms the existing indexing schemes using three real datasets (face, fingerprint, and finger-vein datasets), and is very promising with respect to the accuracy and response time.

An Efficient Biometric Identification in Cloud Computing With Enhanced Privacy Security

Biometric identification has developed rapidly in recent years because of its convenience and reliability. Due to the sensitivity of biometric data, many privacy-preserving biometric identification schemes have been put forward, exploiting either homomorphic encryption or matrix-transformation. However, existing schemes based on homomorphic encryption generally suffer from low computational efficiency, and existing matrix-transformation-based schemes are insufficiently secure. In this paper, we demonstrate that the matrix-transformation-based privacy-preserving biometric identification scheme recently proposed by Zhu et al. is vulnerable to a known-plaintext attack (KPA). To remedy this security flaw, we propose a new privacy-preserving biometric identification scheme, in which the property of the orthogonal matrix and additional randomness are utilized. Security analysis and comparisons indicate that our scheme can resist not only the KPA attack but also the more powerful chosen-plaintext attack (CPA), which is a reasonable attack in practice. Moreover, our scheme enjoys higher computational efficiency than other similar schemes, which implies our scheme can better support a huge database for practical biometric identification, and it also enhances privacy security of sensitive biometric data.

Bloom filter‐based search structures for indexing and retrieving iris‐codes

Large-scale biometric deployments are becoming ubiquitous. The computational workload of the conventional retrieval method, requiring 1:N comparisons in the identification mode, is impractical for such systems. In recent years, many approaches for efficient biometric identification were proposed, but their scalability is often questionable. Furthermore, the lack of a unified methodology for biometric workload reduction reporting often makes a direct benchmark or a thorough evaluation of the proposed schemes cumbersome. We present an iris indexing scheme based on Bloom filters and binary search trees. With a statistical model, the system is shown to be theoretically scalable for arbitrarily many enrollees. We evaluate this system on a combined database from several publicly available datasets, containing a total of 11,936 iris images from 1477 instances. In an open-set identification scenario, the system maintains the biometric performance of an iris-code 1:N baseline – a true positive identification rate of approximately 98% measured at 0.1% false positive identification rate, at only 10% of the baseline workload. In a proof-of-concept multi-iris indexing experiment, the true positive identification rate is further increased to over 99%, without additional workload costs. Lastly, we define several prerequisites necessary for a transparent and comprehensive methodology of biometric workload reduction results dissemination.

Selection of Authentication Systems for Hungarian Health Care, Based on Physiological Study (Part II.)

The actuality of the topic indicated in the title comes from more and more events where the verification of identity might be required. Health-care is an important part of critical national infrastructures. A primary task of the protection of critical information infrastructures consists in the access control of data managed by IT system where the identity-authentication forms an important part. For this reason, the technologies considered suitable for use and their supporting means are listed. After examining and comparing the relevant parameters, the optimum solution for the authentication procedures in the Hungarian health care system is specified. First of all, the modern and efficient biometric identification processes are examined; however, the possession-based Radio Frequency Identification (RFID) as an additional system is also studied. Due to the well-known problems of passwords and chip cards. They were deliberately omitted during the system planning and efforts were made to exclude the human factors from the planned authentication system as much as possible.

Selection of Authentication Systems for Hungarian Health Care, Based on Physiological Study (Part I.)

The actuality of the topic indicated in the title comes from more and more events where the verification of identity might be required. Health-care is an important part of the critical national infrastructures. A primary task of the protection of critical information infrastructures consists in the access control of data managed by IT system where the identity-authentication forms an important part. For this reason, the technologies considered suitable for use and their supporting means are listed.After examining and comparing the relevant parameters, the optimum solution for the authentication procedures in the Hungarian health care system is specified. First of all, the modern and efficient biometric identification processes are examined; however, the possession-based Radio Frequency Identification (RFID), as an additional system, is also studied. Due to the well-known problems of passwords and chip cards, they were deliberately omitted during the system planning and efforts were made to exclude the human factors from the planned authentication system as much as possible.

Effectiveness of Feature Detectionoperators on the Performance of Iris Biometric Recognition System

International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.5, September 2013