Forthcoming articles

 


International Journal of Biometrics

 

These articles have been peer-reviewed and accepted for publication in IJBM, but are pending final changes and are not yet published.

 

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 Articles will not be published until the final proofs are validated by their authors.

 

Articles marked with this shopping trolley icon are available for purchase click on the icon to send an email request to purchase.

 

Articles marked with this Open Access icon are freely available and openly accessible to all without any restriction except use for commercial purposes.

 

Register for our alerting service, which notifies you by email when new issues of IJBM are published online.

 

We also offer RSS feeds which provide timely updates of tables of contents, newly published articles and calls for papers.

 

International Journal of Biometrics (6 papers in press)

 

Regular Issues

 

  • Person Tracking and Segmentation for Human Gait Biometric System   Order a copy of this article
    by Anup Nandy 
    Abstract: Person tracking and segmentation in an unstructured environment provides an increasing demand to solve human identification problems. This paper addresses Mixture of Gaussian (MoG) technique for statistically background modelling and robust human tracking method for deriving an intrinsic gait signature. The front and back leg angles are calculated from the sequence of extracted human motion silhouette frames which are being used as gait features. The training gait database is made with these extracted gait features for ten different training subjects. The Principal Component Analysis (PCA) is applied on derived gait signatures which transforms the input features into a low dimensional feature space. The classification technique is followed by Bayes decision rule coupled with multivariate Gaussian distribution. The results are compared with K-Nearest Neighbor rule and Minimum Distance Classification (MDC) techniques by accuracy and computational cost metric. The experimental verification has been performed on CASIA standard gait database. The Bayes classifier produces an encouraging classification result with minimum misclassification error rate.
    Keywords: Human Gait;Mixture of Gaussian;Segmentation;Person Tracking, Silhouette Extraction;Front and Back Knee Angles; Baye’s Decision Rule; k-Nearest Neighbor; Minimum Distance Classifier
     
  • Person Recognition Using Alternative Hand Geometry   Order a copy of this article
    by Asish Bera 
    Abstract: In this paper a new approach for user recognition is presented, which is based on the geometric features from either left or right hand images. The hand images are collected at unconstrained pose environment. Image normalization is applied at the preprocessing stage. Features are extracted from the normalized images, which are mainly comprised of lengths and widths at different positions of the fingers. A simple classification algorithm has been implemented that is primarily dependent on the ratio of modified minimum distance and number of features, which are matched within a distance threshold. Experimental results of identification and verification are quite acceptable, producing 98.8% identification and 99.6% verification (at 0.55% FAR) of 253 standard subjects which are a blend of both left and right hand images.
    Keywords: Biometric, hand geometry, identification, verification.
     
  • On The Relevance of Using Rhythmic Metrics and SVM to Assess Dysarthric Severity   Order a copy of this article
    by Habiba Dahmani, Sid Ahmed selouani, Noureddine Doghmane, Douglas O'Shaughnessy, Mohamed Chetouani 
    Abstract: Studies of dysarthric speech rhythm have explored the possibility of distinguishing healthy speakers from dysarthric ones. These studies also allowed the detection of different types of dysarthria. The present paper aims at assessing the ability of rhythm metrics to perceive dysarthric severity levels. The study reports on the results of a statistical acoustic investigation using various rhythmic metrics. Among these rhythm features, we propose a new rhythm metric based on an approximation of the speakers rate of articulation. The investigation was carried out on the speech data of American dysarthric patients recorded on the Nemours corpus. The rhythm features are based on two types of segmentation: vocalic/consonantal and voiced/unvoiced interval durations. Results of different classification experiments show that the rhythm-based measures can be used effectively to characterize the dysarthric severity by classifying speakers into their respective categories. Support Vector Machine classification method has been successfully used to perform the assessment of the dysarthria severity level.
    Keywords: Dysarthria, rhythm, Pairwise Variability Index, acoustical analysis, Nemours database, SVM (sup-port vector machine) classification, discriminant analysis.
     
  • Fingerprint Indexing using Minutiae-based Invariable Set of Multidimensional Features   Order a copy of this article
    by Somnath Dey, Om Prakash Singh, Debasis Samanta 
    Abstract: In fingerprint identification, exhaustive search demands a huge response time for large database and hence impractical in many real-life applications. To alleviate this limitation, researchers advocate indexing technique to narrow down the search space. In this work, we investigate three different indexing techniques (linear, clustered and clustered kd-tree) with invariable set of features for a fingerprint identification system. In our approach, we consider local topology of minutiae using two closest points triangle for index key generation. The features are invariant to rotation and scaling and hence, the approach can deal with fingerprints form different devices and sensors. The proposed approach has been tested on NIST DB4 and FVC 2004 databases. Experimental results substantiate the error rate of 0.35%, 1.5% and 2.45% at penetration rate 15% in NIST DB4 for linear search, clustered search and clustered kd-tree search, respectively. For FVC 2004 databases, we attain 0%, 1.36% and 5.45% for FVC2004 DB1, 0%, 2.73% and 4.09% for FVC2004 DB2, 2.27%, 5.0% and 5.91% for FVC2004 DB3 and 0%, 1.36% and 5.0% for FVC2004 DB4 when penetration rate is 15.45% in linear, cluster and clustered kd-tree searches, respectively. The result is indeed comparable to the existing approaches reported in the recent literature.
    Keywords: Biometric system; fingerprint-based identification; fingerprint indexing; biometric-data indexing; data clustering; minutiae feature extraction; index key generation
     
  • Face Recognition in Color JPEG Compressed Domain   Order a copy of this article
    by Alireza Sepas-Moghaddam, Mohammad-Shahram Moin 
    Abstract: JPEG is a widely used compression standard in face recognition systems, which has the capability of reducing the size of images to be stored in facial databases. In a typical face recognition system, compressed coefficients must be fully decompressed to be input to the recognition system which causes a high computational overhead. In order to decrease JPEG decompression overhead, face recognition in JPEG compressed domain has been considered. In spite of the positive role of color features in a pixel domain face recognition system, all of the previous approaches in JPEG compressed domain have been proposed in gray scale space. In this study, for the first time, we investigated the effects of JPEG compressed color features on the efficiency of the state-of-the-art approaches performed in the compressed domain. To achieve this, a comprehensive set of experiments have been conducted on color FEI facial databases. PCA and ICA methods have been used for extracting feature vectors and reducing the dimension of the extracted features and subsequently the distances between the extracted features are calculated. The results in color space have been compared with those in gray space and showed the benefit of using the color features in JPEG compressed domain face recognition, in term of recognition performance. It has also been observed an increase in the class separation and discriminatory power using color compressed domain features over features extracted in gray space.
    Keywords: Face Recognition, JPEG Compressed Domain, Color Features, Color Face Database, Discriminatory Power.
     

Special Issue on: "

ICACNI 2013 "Emerging Trends and Advances in Biometrics"

,"

  • A Secure Palmprint Authentication System using Chaotic Mixing and Watermarking   Order a copy of this article
    by Munaga V N K Prasad, Ilaiah Kavati 
    Abstract: In recent years, there has been a significant increase in the use of biometrics for user authentication. The wide spread use of biometrics in real life applications for user authentication raised the security and privacy concerns of the biometric data. In this paper, a biometric image protection scheme exploiting the properties of chaotic mixing and watermarking technique is proposed. We also proposed an efficient technique for biometric template protection using a random vector. A detailed discussion on the security of the proposed techniques are given. The effectiveness of the proposed method is investigated with application to palmprint authentication. The experimental results evaluated on PolyU and IITD database shows that the proposed system achieves better recognition accuracy and lower error rates while providing protection to the biometric data.
    Keywords: Image security; Template security; Palmprint; Chaotic mixing, Watermarking; Random vector