IET Biometrics
Print ISSN
2047-4938
Online ISSN 2047-4946
Online ISSN 2047-4946
The field of biometric recognition - automated recognition of individuals based on their behavioural and biological characteristics - has now reached a level of maturity where viable practical applications are both possible and increasingly available. The biometrics field is characterised especially by its interdisciplinarity since, while focused primarily around a strong technological base, effective system design and implementation often requires a broad range of skills encompassing, for example, human factors, data security and database technologies, psychological and physiological awareness, and so on. Also, the technology focus itself embraces diversity, since the engineering of effective biometric systems requires integration of image analysis, pattern recognition, sensor technology, database engineering, security design and many other strands of understanding.
Latest news
Content for the first two issues will be available free of charge from this website.
Latest content
-
Critical analysis of adaptive biometric systems
- Author(s): N. Poh; A. Rattani; F. Roli
- + Show Description
-
Hide details
-
p.
179
–187
(9)
Biometric-based person recognition poses a challenging problem because of large variability in biometric sample quality encountered during testing and a restricted number of enrollment samples for training. Solutions in the form of adaptive biometrics have been introduced to address this issue. These adaptive biometric systems aim to adapt enrolled templates to variations in samples observed during operations. However, despite numerous advantages, few commercial vendors have adopted auto-update procedures in their products. This is due in part to the limited understanding and limitations associated with existing adaptation schemes. In view of that the topic of adaptive biometrics has not been systematically investigated, this study works towards filling this gap by surveying the topic from a growing body of the recent literature and by providing a coherent view (critical analysis) of the limitations of the existing systems. In addition, the authors have also identified novel research directions and proposed a novel framework. The overall aim is to advance the state-of-the-art and improve the quality of discourse in this field.
-
Total variability modelling for face verification
- Author(s): R. Wallace; M. McLaren
- + Show Description
-
Hide details
-
p.
188
–199
(12)
This study presents the first detailed study of total variability modelling (TVM) for face verification. TVM was originally proposed for speaker verification, where it has been accepted as state-of-the-art technology. Also referred to as front-end factor analysis, TVM uses a probabilistic model to represent a speech recording as a low-dimensional vector known as an ‘i-vector’. This representation has been successfully applied to a wide variety of speech-related pattern recognition applications, and remains a hot topic in biometrics. In this work, the authors extend the application of i-vectors beyond the domain of speech to a novel representation of facial images for the purpose of face verification. Extensive experimentation on several challenging and publicly available face recognition databases demonstrates that TVM generalises well to this modality, providing between 17 and 39% relative reduction in verification error rate compared to a baseline Gaussian mixture model system. Several i-vector session compensation and scoring techniques were evaluated including source-normalised linear discriminant analysis (SN-LDA), probabilistic LDA and within-class covariance normalisation. Finally, this study provides a detailed comparison of the complexity of TVM, highlighting some important computational advantages with respect to related state-of-the-art techniques.
-
Face recognition for newborns
- Author(s): S. Tiwari; S.K. Singh
- + Show Description
-
Hide details
-
p.
200
–208
(9)
Mixing, abduction and illegal adoption of newborns is a global problem and the research done to solve this problem is minimal. Traditional methods of biometric and non-biometric techniques have their own limitations and fail to provide enough level of security for newborns. The work on adult face recognition has been an active research area in recent years and automatic face recognition for newborns is least reported in the literature. Therefore it is imperative to initiate research, so that future face recognition algorithms will be able to solve this important problem for identification of newborns. The contributions of this research are (i) proposed method, which mitigates the effect of the covariates of newborn face; (ii) providing an analytical and experimental underpinning effect of different facial expressions exhibited by newborns; (iii) preparing newborn face database of 280 individuals with slight variations in pose, illumination and expression. The accuracy of the proposed matching algorithms on the newborn face database with neutral expression is 87.04% and this negates the notion that all newborns look alike.
-
Subface hidden Markov models coupled with a universal occlusion model for partially occluded face recognition
- Author(s): S.-M. Huang; J.-F. Yang
- + Show Description
-
Hide details
-
p.
149
–159
(11)
In this study, a novel face recognition framework based on the grammatical face models has been proposed to tackle partial occlusion problem. The grammatical face model represents a face by five isolated ‘subface’, forehead, eyes, nose, mouth and chin models in cooperation with ‘occlusion’ models. With the creations of ‘subface’ and ‘occlusion’ models, the authors then define a facial grammar to manipulate ‘subface’ and ‘occlusion’ models for constructing various composite face models structurally. Furthermore, the authors also introduce a universal ‘occlusion’ model, which could handle general occlusions to improve the robustness and flexibility of grammatical face models. The proposed face recognition system could overcome two problems. One is to resolve the problem of face recognition with partial occlusions; the other is to overcome a challenge of training face models from occluded face images only. Experimental results carried out on AR facial database reveal that the proposed approach outperforms the state-of-the-art methods.
-
Adaptive discriminative metric learning for facial expression recognition
- Author(s): H. Yan; M.H. Ang; A.N. Poo
- + Show Description
-
Hide details
-
p.
160
–167
(8)
The authors propose in this study a new adaptive discriminative metric learning method for facial expression recognition. Although a number of methods have been proposed for facial expression recognition, most of them apply the conventional Euclidean distance metric to measure the similarity/dissimilarity of face expression images and cannot effectively characterise such similarity/dissimilarity of these images because the intrinsic space of face images usually do not lie in such an Euclidean space. Motivated by the fact that between-class facial images with small differences are more easily mis-classified than those with large differences, the authors propose learning an adaptive metric by imposing large penalties on between-class samples with small differences and small penalties on those samples with large differences simultaneously, such that more discriminative information can be extracted in the learned distance metric for facial expression recognition. Experimental results on three widely used face datasets are presented to demonstrate the efficacy of the proposed method.
-
Optimisation of biometric ID tokens by using hardware/software co-design
- Author(s): J. Liu-Jimenez; R. Sanchez-Reillo; L. Mengibar-Pozo; O. Miguel-Hurtado
- + Show Description
-
Hide details
-
p.
168
–177
(10)
In current society, the necessity of recognising people is increasing every day. Logical or physical access is restricted to authorised users, which in many cases have to provide tokens where their personal information is stored. At the same time, biometrics proposes a feasible solution for the recognition problem. The combination of both solutions is coming up front. However, up till now, owing to processing restrictions, these tokens are just able to store data and perform the last steps of the biometric recognition process. In this study, the authors propose a new system where tokens are based on hardware/software (HW/SW) co-design, which allows computing most of the biometric process in them. This proposal covers several aspects which these systems are subject to, taking advantages of the two platforms they use for reducing computational time or HW area, and also to increase security or minimise misidentification errors. For testing this proposal, an Iris ID token has been implemented, showing different design alternatives adapted to different work scenarios.
-
Oriented diffusion filtering for enhancing low-quality fingerprint images
- Author(s): C. Gottschlich; C.-B. Schönlieb
- + Show Description
-
Hide details
-
p.
105
–113
(9)
To enhance low-quality fingerprint images, we present a novel method that first estimates the local orientation of the fingerprint ridge and valley flow and next performs oriented diffusion filtering, followed by a locally adaptive contrast enhancement step. By applying the authors’ new approach to low-quality images of the FVC2004 fingerprint databases, the authors are able to show its competitiveness with other state-of-the-art enhancement methods for fingerprints like curved Gabor filtering. A major advantage of oriented diffusion filtering over those is its computational efficiency. Combining oriented diffusion filtering with curved Gabor filters led to additional improvements and, to the best of the authors’ knowledge, the lowest equal error rates achieved so far using MINDTCT and BOZORTH3 on the FVC2004 databases. The recognition performance and the computational efficiency of the method suggest to include oriented diffusion filtering as a standard image enhancement add-on module for real-time fingerprint recognition systems. In order to facilitate the reproduction of these results, an implementation of the oriented diffusion filtering for Matlab and GNU Octave is made available for download.
-
Ear biometrics: a survey of detection, feature extraction and recognition methods
- Author(s): A. Pflug; C. Busch
- + Show Description
-
Hide details
-
p.
114
–129
(16)
The possibility of identifying people by the shape of their outer ear was first discovered by the French criminologist Bertillon, and refined by the American police officer Iannarelli, who proposed a first ear recognition system based on only seven features. The detailed structure of the ear is not only unique, but also permanent, as the appearance of the ear does not change over the course of a human life. Additionally, the acquisition of ear images does not necessarily require a person's cooperation but is nevertheless considered to be non-intrusive by most people. Owing to these qualities, the interest in ear recognition systems has grown significantly in recent years. In this survey, the authors categorise and summarise approaches to ear detection and recognition in 2D and 3D images. Then, they provide an outlook over possible future research in the field of ear recognition, in the context of smart surveillance and forensic image analysis, which they consider to be the most important application of ear recognition characteristic in the near future.
-
Effective speaker verification via dynamic mismatch compensation
- Author(s): S. Pillay; A. Ariyaeeinia; P. Sivakumaran; M. Pawlewski
- + Show Description
-
Hide details
-
p.
130
–135
(6)
This paper presents a new approach to condition-adjusted T-norm (CT-Norm) for speaker verification under significant mismatched noise conditions. The study is motivated by the fact that, though the standard CT-Norm method offers enhanced accuracy under mismatched data conditions, its effectiveness reduces with the increased severity of such conditions. The proposed approach attempts to address this challenge by providing a more effective reduction of data mismatch through the incorporation of multi-signal-to-noise ratio (SNR) universal background models (UBMs). The effectiveness of the proposed approach is demonstrated through experiments based on examples of real-world noise. It is shown that the superiority of the approach over CT-Norm is particularly significant for such excessive levels of test data degradation considered in the study as 5 dB SNR and below. The paper provides a description of the characteristics of the proposed approach and details the experimental analysis of its effectiveness under different noise conditions.
-
Framework for managing ageing effects in signature biometrics
- Author(s): M. Erbilek; M. Fairhurst
- + Show Description
-
Hide details
-
p.
136
–147
(12)
This paper investigates and explores the impact of physical ageing in signature biometrics. Experimental performance evaluation, using three different signature databases, is carried out to provide some new insights into the relationship between different practical factors, in particular clarifying the impact on recognition performance of the data collection protocols used and the use of the feature pools underpinning the signature processing. This analysis provides an alternative perspective from which to explore and manage physical ageing effects in signature biometrics. The paper demonstrates that the proposed strategy maximises system accuracy while minimising the performance differential across a population which is heterogeneous with respect to age, and across different databases. The results presented suggest that adoption of the strategy proposed can render a template update procedure less critical than hitherto expected.
