Forthcoming articles


International Journal of Biometrics


These articles have been peer-reviewed and accepted for publication in IJBM, but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.


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International Journal of Biometrics (7 papers in press)


Regular Issues


    by Samuel McKenna, Mark Butler 
    Abstract: Fingerprint scanners are used as a form of control with access limited to the beholder of the ridge detail. However, to what extent these devices are capable of providing that control has not been fully explored. This study tested the reliability of a fingerprint scanner in accessing enrolled fingerprint data, when faced with the challenge of fake fingerprints. Ridge detail casts were crafted from moulds, with Gelatine and Silicone being applied as casting agents. The second stage required participants to place fingerprints on a bottle or tile; these latent impressions were subsequently powdered using Magneta Flake. Provil, a forensic casting material was applied directly onto the powder, creating simulated fingerprints from a latent print. Each of the produced fingerprints then went through a scanning process. All materials tested were able to gain access through the participants enrolled data. This suggests potential unreliability of the fingerprint scanner in storing pertinent data.
    Keywords: Biometrics, fingerprints, scanner, cyber, crime, fingerprint data, identification, digital, verification, theft, cryptography, security, hacking

  • Secure And Efficient Multibiometric Fusion Based Cryptosystem Using Blind Separation Encryption Algorithm   Order a copy of this article
    by Divya Rajalingam 
    Abstract: Multibiometrics is the usage of more than one physiological or behavioral characteristic to identify an individual. Multibiometrics is resilience to spoofing and has low False Acceptance Rate (FAR). However Multibiometrics requires storage of multiple biometric templates for each user which increase the risk to user privacy and system security. The existing Fuzzy logic has several limitations like non-revocability, cross matching and brute force attack. To overcome the above limitations of Fuzzy logic, Hard Fuzzy logic is proposed and implemented for the three biometric features and was simulated. Further authentication is extended to cryptosystem by making use of a novel cryptographic algorithm namely Blind Separation Encryption in which the client and server are authenticated and then the encryption is performed. Discrete Wavelet Transform (DWT) and Sparse Representation are implemented to reduce the redundant data and memory space.
    Keywords: Multibiometric cryptosystem, feature level fusion, fuzzy commitment, Hard Fuzzy Logic, template security, Blind Separation Encryption, Sparse Representation.

  • Multi-Level Fingerprint Continuous Classification for Large Scale Fingerprint Database Using Fractal Analysis   Order a copy of this article
    by Yunfei Zhong, Xiaoqi Peng 
    Abstract: A three-level classification method for fingerprints using fractal analysis was proposed to improve the speed, accuracy, and robustness of an automated recognition system for a large-scale fingerprint database. Low-quality fingerprints were first eliminated via an assessment algorithm with a multi-level progressive discriminant factor, thereby increasing the accuracy of fingerprint recognition. Next, three-level classification was done for fingerprints with acceptable quality. First, the fingerprints were sorted into six categories according to fingerprint types. Second, classification was made based on the number of ridge lines between the singular points of each fingerprint. Third, categorization was done in terms of the fractal dimensions of the stable-quality region of each fingerprint image. With the second and third levels of classification, continuous classification and redundancy retrieval could be achieved, thus effectively improving the accuracy and robustness of the method. The experimental results using the NIST-4 fingerprints database established that the proposed method has various advantages, including fast retrieval speeds, strong adaptability, and great robustness, making it particularly suitable for automated classification and recognition matching for large-scale fingerprint databases.
    Keywords: Fractal Analysis;Box Dimension;Multi-Level Continuous Classification;Redundancy Retrieval;Large-scale Fingerprint Database

  • Forensic Sketch Recognition using User Specific Facial Region   Order a copy of this article
    by Saurabh Singh, Madhavi Sinha 
    Abstract: Forensic sketches play an important role in criminal identification process. These sketches are drawn by forensic artists on the basis of the description provided by an eyewitness or victim. These sketches are publicized to get some clues to reveal the identity of the criminals. A faster way to identify the criminals is to match the forensic sketch with some government agency mug shot database. In the process of drawing a sketch, the description provided by the eyewitness often includes some unique facial details comprising the deviations from an average face. For example some spot on face, mole, scar, cuts etc. Study says that, the information which is more uncommon is more likely to be last in the memory. In this paper, we suggest a sketch to mug shot matching approach called difference vector based matching (DVBM), which utilizes deviations present in facial regions to measure the similarity between a sketch and a mug-shot image. The method is tested over a dataset containing 112 sketches and a large mug-shot gallery of 7112 images. The results generated using DVBM are compared with standard face matchers and show considerable improvement in matching accuracy.
    Keywords: Biometrics; Sketch Recognition; Face Recognition; Specific Features; Image Analysis; Pattern Recognition; Dimensionality Reduction.

  • A Survey of Attacks on Iris Biometric Systems   Order a copy of this article
    by Richa Gupta, Priti Sehgal 
    Abstract: Biometric recognition has several applications that provide reliable solutions to the user authentication problem. Its widespread use and popularity is itself making it prone to several vulnerabilities. Iris is emerging as one of the most popular and accurate biometrics. Due to its inherent advantages and uniqueness, it is gaining popularity as a powerful authentication tool. However, the iris recognition systems may suffer from various attacks at different points during the authentication process. This article intends to review the popular attacks on the iris biometric, which affects its security, and present a survey on the approaches taken by various researchers to mitigate these attacks. The attacks have been divided into three categories, namely - User level, User-System interface level and Stored database attacks. We present an overview of these attacks and analyze the algorithms proposed in the literature that are usually used to secure iris biometric systems from these attacks.
    Keywords: Biometrics, iris recognition, performance evaluation, liveness detection, multimodal system, template attack

  • Human Age Classification using Appearance Features and Artificial Neural Network   Order a copy of this article
    by Jayant Jagtap, Manesh Kokare 
    Abstract: This paper presents a novel method for human age classification via face images by a computer. The proposed method classifies the human face images into four age groups: child, young, adult and senior adult by using appearance features as ageing features and Artificial Neural Network (ANN) as age classifier. The appearance features consists of both shape and textural features. Only two geometric ratios in combination with newly introduced rotation, scale and translation invariant efficient feature face angle are used as shape features. Local Binary Pattern Histogram (LBPH) of regions of interest in face image are used as textural features. The ANN is designed by using two layer feedforward back propagation neural networks. The performance of proposed age classification system is evaluated on face images from FG-NET ageing database and achieved greatly improved accuracy of 91.09% and 88.18% for male and female respectively.
    Keywords: Age classification; Appearance features; Artificial neural networkrn(ANN); Local Binary Pattern Histogram (LBPH).

  • Undecimated Discrete Wavelet Transform for Touchless 2D Fingerprint Identification   Order a copy of this article
    by Salah Ahmed Saeed Othman, Tarik Boudghene Stambouli 
    Abstract: Several recent research efforts in biometrics have focused on developing the touchless fingerprint identification system. Most of them using imaging resulting from cameras and mobile devices. The acquired images are firstly subjected to robust preprocessing steps to localize region of interest in order to extract its features. In the literature, touchless fingerprint features are generally based on algorithms designed for minutiae analysis in touch-based images. Because of perspective distortions and deformations in the samples, minutiae-based techniques can obtain poor results. This paper investigates multi-resolution decomposition features to overcome the limitations of using traditional minutiae algorithms in term of accuracy and matching speed. These decompositions are implemented on Hong Kong polytechnic university 2D touchless fingerprint database that contains 10080 images. Experimental results illustrate successful use of Undecimated Discrete Wavelet Transform (UDWT) and Discrete Wavelet Packet Transform (DWPT) which give better performance than Discrete Wavelet Transform (DWT) and minutiae based method with less calculation cost.
    Keywords: biometrics; touchless fingerprint identification; fingerprint features extraction; wavelet transform; multi-resolution decomposition; minutiae feature.