Forthcoming articles

 


International Journal of Biometrics

 

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International Journal of Biometrics (8 papers in press)

 

Regular Issues

 

  • Fingerprint Indexing via BRIEF Minutia Descriptors   Order a copy of this article
    by Robert Pollak, Roland Richter 
    Abstract: We use BRIEF binary local image descriptors as minutia descriptors for indexing of biometric fingerprint databases. Tests with varying descriptor size and parametrization are performed on a proprietary database. Compared with the speed of an implementation of conventional minutiae matching, we find that BRIEF descriptors are fast enough for database indexing. The tested descriptors outperform two other image descriptors (LBP, HoG) from recent literature with respect to matching rates and average penetration rates.
    Keywords: biometrics; fingerprints; indexing; BRIEF; image descriptors.

  • Mutually Reinforcing Motion-Pose Framework for Pose Invariant Action Recognition   Order a copy of this article
    by Manoj Ramanathan, Wei-Yun Yau, Nadia Magnenat Thalmann, Eam Khwang Teoh 
    Abstract: Action recognition from videos has many potential applications. However, there are many unresolved challenges, such as pose-invariant recognition, robustness to occlusion and others. In this paper, we propose to combine two most important characteristics of an action, motion of body parts and specific canonical poses observed in a novel mutually reinforcing framework to achieve pose-invariant action recognition. The proposed framework consists of two components. The first component is the pose-invariant feature extraction that captures the motion of body parts needed for action recognition. This forms the forward propagation motion path of our framework that recognizes an initial action using the extracted features. Each action is characterized by specific canonical stick poses. Given the training videos of an action, we propose an algorithm to extract a dictionary of normalized canonical stick poses. The second component of the framework is the pose hypothesis generation scheme that compares each of the extracted canonical sticks of the initial action recognized with the video frame to identify the most likely canonical stick pose. We use the identified canonical stick pose in the frame to improve the pose-invariant motion feature extraction. To capture the temporal dynamics of an action, we introduce temporal stick features computed using the stick poses obtained. This pose-based component acts as the feedback path in our framework. The combination of pose-invariant kinematic features from the forward and feedback paths together with the temporal stick features are used to recognize the action, thus forming a mutually reinforcing framework that repeats until the action recognition result converges. The proposed mutual reinforcement framework is capable of handling changes in posture of the person, occlusion and partial view-invariance. We perform experiments on several benchmark datasets which showed the performance of the proposed algorithm and its ability to handle pose variation and occlusion.
    Keywords: Action recognition; pose-invariant motion feature; canonical stick poses; mutual reinforcement framework.

  • An Overlap based Human Gait Cycle Detection   Order a copy of this article
    by Sugandhi K., FARHA FATINA WAHID, Nikesh P, Raju G 
    Abstract: Identification of a person by his/her style of walking is referred as gait recognition. Gait is one among the biometric used for Human identification. In gait recognition, an inevitable step for accurate feature extraction is gait cycle detection. In this paper, a novel gait cycle detection algorithm based on the concept of overlap between legs during locomotion is proposed. To identify overlap, zero-crossing counts of silhouette frames as well as bottom halves of silhouette frames are considered. The efficiency of this algorithm is tested using normal walking sequence of subjects with 900 viewing angle from CASIA B as well as TUM-IITKGP human gait databases. The results obtained shows that gait cycle can be easily and efficiently detected with zero-crossing count of silhouette frames. Further zero-crossing counts taken from bottom halves of silhouette frames gives better performance.
    Keywords: Gait; Gait cycle; Overlap; silhouette; zero-crossing.

  • On the performance improvement of non-cooperative iris biometrics using segmentation and feature selection techniques   Order a copy of this article
    by A. Alice Nithya, C. Lakshmi 
    Abstract: In this work, an improved segmentation methodology and a novel statistical dependency-based backward feature selection algorithm are proposed. From the input eye image, iris boundary is identified using Circular Hough Transform. A bounding box is defined using the radius obtained followed by iterative thresholding techniques to eliminate specular reflections, eyelids, eyelashes and pupil region. First-order and second-order statistical features are extracted from the segmented iris. For the first time, the statistical measure, Chi-square value is computed from GLCM as a new novel feature from iris images. Statistical dependency-based backward feature selection (SDBFS) algorithm is used to reduce the feature vector size. By operating on local features in reduced search space, computation complexity of segmentation is reduced with less mislocalisation count and eliminates pupil dilation effects. Results of SDBFS show the usefulness of minimal-useful features. Experimental results conducted on CASIA V1, V3-interval and UBIRIS V1 datasets show that statistical features in non-ideal iris images outperform some of the state-of-the-art methods.
    Keywords: iris recognition; Circular Hough Transform; grey level co-occurrence matrix; GLCM; backward feature selection; chi-square value; segmentation; statistical dependency.
    DOI: 10.1504/IJBM.2019.10016803
     
  • Human age classification using appearance and facial skin ageing features with multi-class support vector machine   Order a copy of this article
    by Jayant Jagtap, Manesh Kokare 
    Abstract: Human age classification via face images is not only difficult for human being but also challenging for a machine. But, because of potential applications in the field of computer vision, this topic has attracted attention of many researchers. In this paper, a novel two stage age classification framework based on appearance and facial skin ageing features with multi-class support vector machine (M-SVM) is proposed to classify the face images into seven age groups. Appearance features consist of shape features such as, geometric ratios and face angle and facial skin textural features extracted by using local Gabor binary pattern histogram (LGBPH). Facial skin ageing features consist of facial skin textural features and wrinkle analysis. The proposed age classification framework is trained and tested with face images collected from FG-NET ageing database and PAL face database and achieved greatly improved age classification accuracy of 94.45%.
    Keywords: appearance features; facial skin ageing features; local Gabor binary patterns histogram; LGBPH; wrinkle analysis; age classification framework; multi-class support vector machine; M-SVM.
    DOI: 10.1504/IJBM.2019.10016805
     
  • Bone- and air-conduction speech combination method for speaker recognition   Order a copy of this article
    by Satoru Tsuge, Shingo Kuroiwa 
    Abstract: In this paper, first, we report speaker recognition performance using bone-conduction speech based on an i-vector-based speaker recognition system, which is the current state-of-the-art method. In addition, we propose three speaker recognition methods combining bone-conduction speech and air-conduction speech: a feature combination method, a speaker model combination method, and a similarity score combination method. To evaluate the proposed methods, we conducted speaker recognition experiments using a part of a large speech corpus constructed by the National Research Institute of Police Science, Japan. Experimental results show the bone-conduction speech performs almost the same as the air-conduction speech when the enrolment data and evaluation data are collected in the same session. In addition, all proposed methods improved the speaker recognition performance of air- and bone-conduction speech in the experiments. From these results, we conclude that fusing air- and bone-conduction speech improves the speaker recognition performance.
    Keywords: speaker recognition; bone-conduction speech; air-conduction speech; i-vector; personal authentication systems; biometrics; bone-conductive microphone; condenser microphone; speech processing; session variability; new speech sensors.
    DOI: 10.1504/IJBM.2019.10016806
     
  • A novel discriminant multiscale representation for ear recognition   Order a copy of this article
    by Hakim Doghmane, Abdelhani Boukrouche, Larbi Boubchir 
    Abstract: This paper proposes a novel representation for ear recognition. It introduces a new alternative of binarised statistical image features based on multiscale framework. The proposed representation allows capturing the image content at multiple resolutions. The recognition accuracy can be enhanced by the following steps. First, for a given ear image, a set of multiscale response images are derived from the bank of binarised statistical image features (B-BSIF) filter. Second, the obtained response images are summarised by concatenating their histograms, which are obtained at each scale. Finally, a discriminative ear image representation is build by projecting the above mentioned histograms into a linear discriminant analysis subspace. The proposed representation is applied on three public databases: IIT Delhi-1, IIT Delhi-2 and USTB. The obtained recognition accuracy confirms its performance than the recent existing methods.
    Keywords: ear recognition; multi-resolution analysis; K-NN; whitened linear discriminant analysis; WLDA; B-BSIF.
    DOI: 10.1504/IJBM.2019.10016808
     
  • A survey on different continuous authentication systems   Order a copy of this article
    by S. Ayeswarya, Jasmine Norman 
    Abstract: There has been significant research in the provision of trustworthy initial login user authentication, however, there is still need for continuous authentication during a user session. Most mobile devices and computer systems authenticate a user only at the initial login session and do not take steps to recognise whether the present user is still the initial authorised user or an imposter pretending to be a valid user. Therefore, a system to check the identity of the user continuously throughout the whole session is necessary. To ensure the authenticity of the user during their whole login session, a continuous user authentication mechanism is required. In this paper, an overview of different continuous authentication methods is presented along with a discussion on the merits and demerits of the available approaches. This paper also discusses the understanding of the emerging necessities and open problems in continuous user authentication system.
    Keywords: continuous authentication; continuous verification; biometrics; data privacy; security.
    DOI: 10.1504/IJBM.2019.10016811