International Journal of Biometrics (9 papers in press)
- PIN Generation Using EEG: A Stability Study
by Ramaswamy Palaniappan, Kenneth Revett
Abstract: In a previous study, it has been shown that brain activity, i.e. electroencephalogram (EEG) signals can be used to generate personal identification number (PIN). The method was based on brain-computer interface (BCI) technology using a P300-based BCI approach and showed that a single channel EEG was sufficient to generate PIN without any error for three subjects. The advantage of the method is obviously its fraud resistance compared to conventional methods of PIN generation such as entering the numbers using a keypad. Here, we investigate the stability of these EEG signals when used with a neural network classifier, i.e. to investigate the changes in the performance of the method over time. Our results, based on recording conducted over a period of three months, indicate that a single channel is no longer sufficient and a multiple electrode configuration is necessary to maintain acceptable performances. Alternatively, a recording session to retrain the neural network classifier can be conducted on shorter intervals, though practically this might not be viable.
Keywords: Biometrics; Brian-computer Interface; Electroencephalogram; Personal Identification Number; Neural Networks
- Biometric verification of a user based on eye movements
by Youming Zhang, Martti Juhola
Abstract: The biometric verification of users of computers or other machines is usually performed with fingerprints, face images or even iris or palm images. Eye movements have seldom been studied for biometric verification, although in the future their use will perhaps extend from laboratory applications to integrated parts of computer interfaces. Eye movements have long been studied in medical and psychological applications. We have noticed that there are differences between saccade eye movements of individuals, even in a group of young people approximately of the same age. We measured saccades from 68 voluntary subjects by performing the same stimulation for each to obtain comparable data. We tested two verification conditions: (1) an authenticated user vs. all other subjects and (2) an impostor vs. an authenticated user and others. Thorough randomized classifications with discriminant analysis, k-d tree and k nearest-neighbour searching, decision trees and the na
Keywords: Eye movements; saccade; user verification; signal analysis; classification
- A Non-Invertible Cancellable Fingerprint Construct based on Compact Minutiae Patterns
by Vedrana Krivokuca, Waleed Abdulla, Akshya Swain
Abstract: This paper proposes a new, privacy-preserving fingerprint construct, which consists of a single pattern created from a small subset of minutiae from the corresponding minutiae template. The sparsity of the resulting feature vector ensures that it cannot be used to reconstruct the underlying fingerprint image, while simultaneously allowing for the cancellability of compromised patterns. The proposed construct is, therefore, inherently a non-invertible cancellable fingerprint template protection scheme. Experimental results on the recognition accuracy of patterns consisting of three, four, and five minutiae were very encouraging: an FRR of 0.4% or less was achieved for all three pattern sizes, while the FAR ranged from 2.1% for 5-minutia patterns to 6.5% for 3-minutia patterns. It took about 0.1 seconds to verify a single person, regardless of the pattern size. These results suggest that the proposed fingerprint construct is suitable for deployment in fingerprint-based civilian authentication applications.
Keywords: biometrics; fingerprints; minutiae; minutiae template; minutiae patterns; minutiae structures; fingerprint representation; fingerprint template protection; fingerprint image reconstruction; cancellable biometrics; non invertible; fingerprint matching; verification; authentication; recognition.
- Fingerprint Quality Assessment Based On Wave atoms Transform
by Leila Boutella, Amina Serir
Abstract: Fingerprint image quality is of great importance for an automatic fingerprint identification system (AFIS), and affects its performance. In this paper, a new fingerprint image quality measure based on Wave atoms transform is proposed. Indeed, fingerprint features are extracted by exploiting Wave atoms multiscale and multi-directional properties. Hence, for good quality block, the singularities are concentrated in a same directional subbands. In the opposite, for poor quality, the singularities are scattered over several directional subbands. In order to evaluate the performance of the algorithm, FVC2002 databases have been considered. The results show that this method has a serious potential in fingerprint quality evaluation and will improve the performance and effectiveness of AFIS.
Keywords: Fingerprint quality assessment, Wave atoms transform, Multi-directional analysis.
- Functional Data Analysis in the Use of Eyebrow Shape as a Biometric Indicator in Face Recognition
by Midori Albert, Cuixian Chen, Yishi Wang, Yaw Chang
Abstract: This paper reports the use of eyebrow shape as a point feature for face recognition in the identity sciences. An approach to quantifying eyebrow shape and results of an experiment to test how human perceptions of eyebrow shape (qualitative analyses) compare with quantitative analyses (i.e., computer generated algorithms) of eyebrow shape are discussed. The aim is to develop a method for face identification using a point feature, such as the eyebrow, inasmuch as face images used in forensic face image comparisons may be obscured due to sunglasses or head coverings, or indiscernible due to pose or lighting issues. Results showed that functional data analysis was successful in interpreting eyebrow shape from digitized face images, and that computer-classified (i.e., quantitative analyses) eyebrow shapes were more reliable than human perception (i.e., qualitative analyses) as a relatively high level of human subjectivity was evident from findings of a two-trial experiment on eyebrow classification.
Keywords: Functional Data Analysis; Face Recognition; Eyebrow Shape; Biometric Indicator; Biometric; Identity Sciences; Point Feature
- Personal Identification using Local Gaussian Quadrature Filter Pair Phase Quantization of Hand Vein Images
by T Anantha Kumar, K Premalatha
Abstract: Hand vein pattern is a biometric feature in which the actual pattern is the shape of vein network and its characteristics are the vein features. This paper proposes a new approach uses local phase quantization with Gaussian quadrature filter pair for hand dorsal vein identification. The proposed work extracts the phase information computed locally in a window for every pixel position by employing Gaussian quadrature filter pair. The phases of six frequency coefficients are quantized and it is used to form a descriptor code for the local region. Whitening transformation is used to decorrelate these local descriptors and a histogram is generated for every pixel which describes the local pattern. The proposed work is experimented with minimum distance classifiers and results are examined for recognition rate, False Acceptance Rate (FAR), False Rejection Rate (FRR) and Equal Error Rate (EER). Experiment results show that the proposed work outperforms the existing methods.
Keywords: Dorsal hand vein; Gaussian filter; Phase quantization; Decorrelation; Whitening transformation.
- Person Tracking and Segmentation for Human Gait Biometric System
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; Bayeâ€™s Decision Rule; k-Nearest Neighbor; Minimum Distance Classifier
- Person Recognition Using Alternative Hand Geometry
by Asish Bera
Abstract: â€” 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.
Special Issue on: "
ICACNI 2013 "Emerging Trends and Advances in Biometrics"
- A Secure Palmprint Authentication System using Chaotic Mixing and Watermarking
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