On Acquisition and Analysis of a Dataset Comprising of Gait, Ear and Semantic data

In outdoor scenarios such as surveillance where there is very little control over the environments, complex computer vision algorithms are often required for analysis. However constrained environments, such as walkways in airports where the surroundings and the path taken by individuals can be controlled, provide an ideal application for such systems. Figure 1.1 depicts an idealised constrained environment. The path taken by the subject is restricted to a narrow path and once inside is in a volume where lighting and other conditions are controlled to facilitate biometric analysis. The ability to control the surroundings and the flow of people greatly simplifes the computer vision task, compared to typical unconstrained environments. Even though biometric datasets with greater than one hundred people are increasingly common, there is still very little known about the inter and intra-subject variation in many biometrics. This information is essential to estimate the recognition capability and limits of automatic recognition systems. In order to accurately estimate the inter- and the intra- class variance, substantially larger datasets are required [40]. Covariates such as facial expression, headwear, footwear type, surface type and carried items are attracting increasing attention; although considering the potentially large impact on an individuals biometrics, large trials need to be conducted to establish how much variance results. This chapter is the first description of the multibiometric data acquired using the University of Southampton's Multi-Biometric Tunnel [26, 37]; a biometric portal using automatic gait, face and ear recognition for identification purposes. The tunnel provides a constrained environment and is ideal for use in high throughput security scenarios and for the collection of large datasets. We describe the current state of data acquisition of face, gait, ear, and semantic data and present early results showing the quality and range of data that has been collected. The main novelties of this dataset in comparison with other multi-biometric datasets are: 1. gait data exists for multiple views and is synchronised, allowing 3D reconstruction and analysis; 2. the face data is a sequence of images allowing for face recognition in video; 3. the ear data is acquired in a relatively unconstrained environment, as a subject walks past; and 4. the semantic data is considerably more extensive than has been available previously. We shall aim to show the advantages of this new data in biometric analysis, though the scope for such analysis is considerably greater than time and space allows for here

Biodegradation of high-concentration isopropanol by a solvent-tolerant thermophile, Bacillus pallidus

The aerobic biodegradation of high-concentration, to 24 g l –1 , 2-propanol (IPA) by a thermophilic isolate ST3, identified as Bacillus pallidus , was successfully carried out for the first time. This solvent-tolerant B. pallidus utilized IPA as the sole carbon source within a minimal salts medium. Cultivation was carried out in 100-ml shake flasks at 60°C and compared with cultivation within a 1-l stirred tank reactor (STR). Specific growth rate () was about 0.2 h–1 for both systems, with a maximum cell density of 2.4 x 10 8 cells ml–1 obtained with STR cultivation. During exponential growth and stationary phase, IPA biodegradation rates were found to be 0.14 and 0.02 g l –1h–1, respectively, in shake-flask experiments, whereas corresponding values of 0.09 and 0.018 g l –1h–1 were achievable in the STR. Generation of acetone, the major intermediate in aerobic IPA biodegradation, was also monitored as an indicator of microbial IPA utilization. Acetone levels reached a maximum of 2.2–2.3 g l–1 after 72 and 58 h for 100-ml and 1-l systems, respectively. Both IPA and acetone were completely removed from the medium following 160 and 175 h, respectively, during STR growth, although this was not demonstrated within shake-flask reactions. Growth of B. pallidus on acetone or IPA alone demonstrated that the maximum growth rate () obtainable was 0.247 h–1 at 4 g l–1 acetone and 0.202 h–1 at 8 g l–1 IPA within shake-flask cultivation. These results indicate the potential of the solvent-tolerant thermophile B. pallidus ST3 in the bioremediation of hot solvent-containing industrial waste streams

3D Morphable Model Construction for Robust Ear and Face Recognition

Recent work suggests that the human ear varies significantly between different subjects and can be used for identification. In principle, therefore, using ears in addition to the face within a recognition system could improve accuracy and robustness, particularly for non-frontal views. The paper describes work that investigates this hypothesis using an approach based on the construction of a 3D morphable model of the head and ear. One issue with creating a model that includes the ear is that existing training datasets contain noise and partial occlusion. Rather than exclude these regions manually, a classifier has been developed which automates this process. When combined with a robust registration algorithm the resulting system enables full head morphable models to be constructed efficiently using less constrained datasets. The algorithm has been evaluated using registration consistency, model coverage and minimalism metrics, which together demonstrate the accuracy of the approach. To make it easier to build on this work, the source code has been made available online

Can gait biometrics be spoofed?

Gait recognition is a relatively new biometrics and no effort has yet been devoted to studying spoofing attacks against video-based gait recognition systems. Spoofing occurs when a person tries to imitate the clothing and/or walking style of someone else in order to gain illegitimate access and advantages. To gain insight into the performance of current gait biometric systems when confronted to spoofing attacks, we provide in this paper the first investigation in the research literature on how clothing can be used to spoof a target and evaluate the performance of two state-of-the-art recognition methods on a novel gait spoofing database recorded at the University of Southampton. The experiments point out very interesting findings that can be used as a reference for future investigations by the research community

Targeted impersonation as a tool for the detection of biometric system vulnerabilities

This paper argues that biometric verification evaluations can obscure vulnerabilities that increase the chances that an attacker could be falsely accepted. This can occur because existing evaluations implicitly assume that an imposter claiming a false identity would claim a random identity rather than consciously selecting a target to impersonate. This paper shows how an attacker can select a target with a similar biometric signature in order to increase their chances of false acceptance. It demonstrates this effect using a publicly available iris recognition algorithm. The evaluation shows that the system can be vulnerable to attackers targeting subjects who are enrolled with a smaller section of iris due to occlusion. The evaluation shows how the traditional DET curve analysis conceals this vulnerability. As a result, traditional analysis underestimates the importance of an existing score normalisation method for addressing occlusion. The paper concludes by evaluating how the targeted false acceptance rate increases with the number of available targets. Consistent with a previous investigation of targeted face verification performance, the experiment shows that the false acceptance rate can be modelled using the traditional FAR measure with an additional term that is proportional to the logarithm of the number of available targets