The authors present an improved feature selection solution for the view-invariant gait recognition problem, based on their previously proposed method called view-invariant feature selector (ViFS), which automatically reconstruct an optimised gallery template from a set of multi-view gallery templates. They improved ViFS by introducing a constraint to make sure that the reconstructed features have the same scale as the original features, thus reducing the number of misclassifications caused by data misalignment. They evaluate the improved ViFS on the CASIA B and OU-ISIR large population datasets by performing a wide range of comparative studies in order to explore and confirm its effectiveness. Evaluation results indicate that the proposed framework is very effective for view-invariant gait recognition tasks.

Recently, palmprints have been broadly reported in the literature as an effective biometric modality. Although scale-invariant feature transform (SIFT)-based features have been proven to be robust against image transformations and deformations, SIFT has not been as successful as other methods in palmprint recognition. In fact, SIFT-based identification has been widely criticised in biometrics due to its high false matching rate. To overcome this weakness, a new filtering method for SIFT-based palmprint matching, called the self-geometric relationship-based filter (SGR-filter) is presented. While existing SIFT matching considers only the relationship between the SIFT points of the query image, on one hand, and their corresponding points in the reference image, on the other hand, SGR-filtering further takes into account the geometric relationship between SIFT points within the query image in comparison with the relationship of the corresponding matched points in the reference image. Assessed with the proposed SGR-filter on various datasets, the SIFT-based palmprint recognition system has been shown to deliver significantly higher performance when compared with the conventional SIFT matching as well as another related key-points filtering technique. Furthermore, experimental results on a number of different full and partial palmprint datasets have shown the superiority of the proposed system over state-of-the-art techniques.

An imperceptible visible watermarking (IVW) algorithm is proposed to deliver auxiliary information about the visual contents, including ownership information. The proposed algorithm overcomes several inconveniences presented in previously reported IVW algorithms using the just noticeable difference (JND) criterion, invisible watermarking and binarisation function. The proposed algorithm consists of the visible watermark embedding and the exhibition stages. The JND criterion is used to embed visible watermark pattern in an imperceptible manner by the human visual system, while invisible watermarking based on the discrete cosine transform is used to share crucial parameters between both stages, which makes possible the watermark visualisation without any side information. In the watermark exhibition stage, the imperceptibly embedded visible watermark pattern is exhibited by the binarisation function. The proposed algorithm can be applied to any class of images with different characteristics and several visible watermark patterns can be embedded into an input image. Evaluation results show that performance of the proposed algorithm is better compared with previously reported algorithms from several practical points of view.

Non-reference image quality measures (IQM) as well as their associated natural scene statistics (NSS) are used to distinguish real biometric data from fake data as used in presentation/sensor spoofing attacks. An experimental study shows that a support vector machine directly trained on NSS as used in blind/referenceless image spatial quality evaluator provides highly accurate classification of real versus fake iris, fingerprint, face, and fingervein data in generic manner. This contrasts to using the IQM directly, the accuracy of which turns out to be rather data set and parameter choice-dependent. While providing very low average classification error rate values for complete training data, generalisation to unseen attack types is difficult in open-set scenarios and obtained accuracy varies in almost unpredictable manner. This implies that for each given sensor/attack set-up, the ability of the introduced methods to detect unseen attacks needs to be assessed separately.

Since its introduction in 2014, the face morphing forgery (FMF) attack has received significant attention from the biometric and media forensic research communities. The attack aims at creating artificially weakened templates which can be successfully matched against multiple persons. If successful, the attack has an immense impact on many biometric authentication scenarios including the application of electronic machine-readable travel document (eMRTD) at automated border control gates. We extend the StirTrace framework for benchmarking FMF attacks by adding five issues: a novel three-fold definition for the quality of morphed images, a novel FMF realisation (combined morphing), a post-processing operation to simulate the digital image format used in eMRTD (passport scaling 15 kB), an automated face recognition system (VGG face descriptor) as additional means for biometric quality assessment and two feature spaces for FMF detection (keypoint features and fusion of keypoint and Benford features) as additional means for forensic quality assessment. We show that the impact of StirTrace post-processing operations on the biometric quality of morphed face images is negligible except for two noise operators and passport scaling 15 kB, the impact on the forensic quality depends on the type of post-processing, and the new FMF realisation outperforms the previously considered ones.

Morphing techniques can be used to create artificial biometric samples or templates, which resemble the biometric information of two or more individuals in signal and feature domain. If morphed biometric samples or templates are infiltrated to a biometric recognition system, the subjects contributing to the morphed sample can be both successfully verified against a single enrolled template. Hence, the unique link between individuals and their biometric reference data is not warranted. This leads to serious security gaps in biometric applications, in particular, the issuance and verification process of electronic travel documents. Recently, different biometric systems have been attacked using morphed biometric samples. However, so far a systematic approach to predict the vulnerability of the system to such attacks has not been proposed. In this work, the authors present a framework to evaluate the vulnerability of biometric systems to attacks using morphed biometric information. Based on a biometric system's mated/non-mated score distribution and its decision threshold, a theoretical vulnerability assessment is proposed. In an experimental evaluation, the vulnerability of a face and an iris recognition system is quantified based on the presented framework. Obtained results are verified against real attacks based on morphed face images and morphed iris-based templates.

Brain signals have long been studied within various fields like medical, physiotherapy, and neurology for many years. One of the main reasons for this interest is to better understand brain diseases like Parkinson's, Schizophrenia, Alzheimer's, epilepsy, spinal cord injuries, and stroke among others. More recently, they have been used in brain–computer interface systems for rehabilitation, entertainment, and assistance applications. Even with the growing interest in clinical applications, the scientific community has only recently investigated the possibility of using brain signals as a potential biometric feature that can be used in people authentication and recognition systems. In this research, the authors have studied the use of brain signals acquired using electroencephalogram (EEG) during both eyes open and eyes closed states for identification based on a large dataset of 109 subjects. The use of a novel mind relaxation metric to determine the optimum epochs to select for the classification and verification has generated very high classification results, in the range of 97–99% based on a single channel. The approach has also been validated against another dataset to verify its consistency and repeatability. The results demonstrate that it is possible to move towards a single-channel biometric identification system with a very high level of reliability and accuracy.

Dental panoramic radiographic images are commonly used as biometrics for human identification. In this study, a novel method is presented for identifying humans by matching 2D panoramic dental X-ray images. Each tooth is first identified and labeled with support vector machines and probabilistic graphical models. Missing teeth and dental restorations are also detected. Then, matching scores between images are calculated according to tooth-wise appearance and geometric similarities by taking dental restorations into account. The method is tested on a dataset including 206 dental panoramic X-ray images of 170 different subjects. The proposed method has 81% rank-1 accuracy and 89% rank-2 accuracy.

To confirm an individual's identity accurately and reliably iris recognition systems analyse the texture that is visible in the iris of the eye. The rich random pattern of the iris constitutes a powerful biometric characteristic suitable for biometric identification in large-scale deployments. Identification attempts or deduplication checks require an exhaustive one-to-many comparison. Hence, for large-scale biometric databases with millions of enrollees, the time required for a biometric identification is expected to significantly increase. In this study, the authors analyse techniques to accelerate Hamming distance-based comparisons of binary biometric reference data, i.e. iris-codes, in large-scale iris recognition systems, which preserve the biometric performance. The focus is put on software-based optimisations, an efficient two-step iris-code alignment process referred to as TripleA, and a combination thereof. Benchmarking the throughput and identifying potential bottlenecks of a portable commodity hardware-based iris recognition system is of particular interest. Based on the conducted experiments the authors point out practical boundaries of large-scale comparisons in central processing unit-based iris recognition systems, bridging the gap between the fields of iris recognition and software design.

Re-identification enables the tracking of the person taken from different disjoint camera aspects either from online or retrospectively for recognition of his or her visual appearance. Here a new method is proposed for person re-identification, taking into consideration the pose of the person as the primary factor, with multiple features being extracted from significant portions. Then angle-based pose priority has applied for matching and identification more robust to viewpoint. Their proposed method helps to reduce the number of images which are redundant in the training phase as well as the number of matching process in the test phase. The strength of the proposed method is demonstrated on three different benchmark databases containing more than 1000 person-images under variations in illumination, viewpoint and occlusion. The experimental results show that the proposed approach provides a higher recognition rates for all the issues of identification process. Finally, the results prove the superiority of the proposed method over other re-identification methods both in terms of visual and quantitative comparisons.

The accuracy of biometric recognition in unconstrained scenarios has been a major concern for a large number of researchers. Despite such efforts, no system can recognise in a fully automated manner human beings in totally wild conditions such as in surveillance environments. Several sets of degraded data have been made available to the research community, where the reported performance by state-of-the-art algorithms is already saturated, suggesting that these sets do not reflect faithfully the conditions in such hard settings. To this end, the authors introduce the QUIS-CAMPI data feed, comprising samples automatically acquired by an outdoor visual surveillance system, with subjects on-the-move and at-a-distance (up to 50 m). Also, they supply a high-quality set of enrolment data. When compared to similar data sources, the major novelties of QUIS-CAMPI are: (i) biometric samples are acquired in a fully automatic way; (ii) it is an open dataset, i.e. the number of probe images and enroled subjects grow on a daily basis; and (iii) it contains multi-biometric traits. The ensemble properties of QUIS-CAMPI ensure that the data span a representative set of covariate factors of real-world scenarios, making it a valuable tool for developing and benchmarking biometric recognition algorithms capable of working in unconstrained scenarios.

This study presents an illumination pre-processing method termed as ‘Discrete wavelet transform enhanced contrast limited adaptive histogram equalisation’ (DWT E-CLAHE) to recognise the front view facial images in the difficult light conditions. A recent image enhancement method CLAHE-DWT motivates to combine the two-dimensional discrete wavelet transform (2D DWT) and CLAHE. The DWT E-CLAHE is implemented as follows: The original image is enhanced using the Gamma intensity correction (GIC); then split into low-frequency and high-frequency components using 2D DWT; finally, to the low-frequency components, the logarithmic transform, GIC and CLAHE are applied in the sequential order. The face recognition of DWT E-CLAHE is made using Gabor magnitude features. The face recognition of CLAHE-DWT is implemented for the first time. The experimental results of DWT E-CLAHE in the various face databases prove the following: (i) The proper selection of parameters of DWT E-CLAHE improves the brightness and contrast of the image. (ii) The recognition accuracy of DWT E-CLAHE in the Carnegie Mellon University (CMU) Pose, Illumination, and Expression (PIE) and Extended Yale B databases are extremely good, since the brightness and contrast are improved significantly. (iii) The performance comparison of DWT E-CLAHE outperforms CLAHE-DWT and state-of-the-art face recognition methods. (iv) DWT E-CLAHE recognises the varying facial expressions.