Soft-biometrics : unconstrained authentication in a surveillance environment

Soft biometrics are characteristics that can be used to describe, but not uniquely identify an individual. These include traits such as height, weight, gender, hair, skin and clothing colour. Unlike traditional biometrics (i.e. face, voice) which require cooperation from the subject, soft biometrics can be acquired by surveillance cameras at range without any user cooperation. Whilst these traits cannot provide robust authentication, they can be used to provide coarse authentication or identification at long range, locate a subject who has been previously seen or who matches a description, as well as aid in object tracking. In this paper we propose three part (head, torso, legs) height and colour soft biometric models, and demonstrate their verification performance on a subset of the PETS 2006 database. We show that these models, whilst not as accurate as traditional biometrics, can still achieve acceptable rates of accuracy in situations where traditional biometrics cannot be applied.

Super-resolved faces for improved face recognition from surveillance video advances in biometrics

Characteristics of surveillance video generally include low resolution and poor quality due to environmental, storage and processing limitations. It is extremely difficult for computers and human operators to identify individuals from these videos. To overcome this problem, super-resolution can be used in conjunction with an automated face recognition system to enhance the spatial resolution of video frames containing the subject and narrow down the number of manual verifications performed by the human operator by presenting a list of most likely candidates from the database. As the super-resolution reconstruction process is ill-posed, visual artifacts are often generated as a result. These artifacts can be visually distracting to humans and/or affect machine recognition algorithms. While it is intuitive that higher resolution should lead to improved recognition accuracy, the effects of super-resolution and such artifacts on face recognition performance have not been systematically studied. This paper aims to address this gap while illustrating that super-resolution allows more accurate identification of individuals from low-resolution surveillance footage. The proposed optical flow-based super-resolution method is benchmarked against Baker et al.’s hallucination and Schultz et al.’s super-resolution techniques on images from the Terrascope and XM2VTS databases. Ground truth and interpolated images were also tested to provide a baseline for comparison. Results show that a suitable super-resolution system can improve the discriminability of surveillance video and enhance face recognition accuracy. The experiments also show that Schultz et al.’s method fails when dealing surveillance footage due to its assumption of rigid objects in the scene. The hallucination and optical flow-based methods performed comparably, with the optical flow-based method producing less visually distracting artifacts that interfered with human recognition.

Determining operational measures from multi-camera surveillance systems using soft biometrics

CCTV and surveillance networks are increasingly being used for operational as well as security tasks. One emerging area of technology that lends itself to operational analytics is soft biometrics. Soft biometrics can be used to describe a person and detect them throughout a sparse multi-camera network. This enables them to be used to perform tasks such as determining the time taken to get from point to point, and the paths taken through an environment by detecting and matching people across disjoint views. However, in a busy environment where there are 100's if not 1000's of people such as an airport, attempting to monitor everyone is highly unrealistic. In this paper we propose an average soft biometric, that can be used to identity people who look distinct, and are thus suitable for monitoring through a large, sparse camera network. We demonstrate how an average soft biometric can be used to identify unique people to calculate operational measures such as the time taken to travel from point to point.

Identifying customer behaviour and dwell time using soft biometrics

In a commercial environment, it is advantageous to know how long it takes customers to move between different regions, how long they spend in each region, and where they are likely to go as they move from one location to another. Presently, these measures can only be determined manually, or through the use of hardware tags (i.e. RFID). Soft biometrics are characteristics that can be used to describe, but not uniquely identify an individual. They include traits such as height, weight, gender, hair, skin and clothing colour. Unlike traditional biometrics, soft biometrics can be acquired by surveillance cameras at range without any user cooperation. While these traits cannot provide robust authentication, they can be used to provide identification at long range, and aid in object tracking and detection in disjoint camera networks. In this chapter we propose using colour, height and luggage soft biometrics to determine operational statistics relating to how people move through a space. A novel average soft biometric is used to locate people who look distinct, and these people are then detected at various locations within a disjoint camera network to gradually obtain operational statistics

Identity theft, computers and behavioral biometrics

The increase of online services, such as eBanks, WebMails, in which users are verified by a username and password, is increasingly exploited by Identity Theft procedures. Identity Theft is a fraud, in which someone pretends to be someone else is order to steal money or get other benefits. To overcome the problem of Identity Theft an additional security layer is required. Within the last decades the option of verifying users based on their keystroke dynamics was proposed during login verification. Thus, the imposter has to be able to type in a similar way to the real user in addition to having the username and password. However, verifying users upon login is not enough, since a logged station/mobile is vulnerable for imposters when the user leaves her machine. Thus, verifying users continuously based on their activities is required. Within the last decade there is a growing interest and use of biometrics tools, however, these are often costly and require additional hardware. Behavioral biometrics, in which users are verified, based on their keyboard and mouse activities, present potentially a good solution. In this paper we discuss the problem of Identity Theft and propose behavioral biometrics as a solution. We survey existing studies and list the challenges and propose solutions.

A generic framework and runtime environment for development and evaluation of behavioral biometrics solutions

Increasing use of computerized systems in our daily lives creates new adversarial opportunities for which complex mechanisms are exploited to mend the rapid development of new attacks. Behavioral Biometrics appear as one of the promising response to these attacks. But it is a relatively new research area, specific frameworks for evaluation and development of behavioral biometrics solutions could not be found yet. In this paper we present a conception of a generic framework and runtime environment which will enable researchers to develop, evaluate and compare their behavioral biometrics solutions with repeatable experiments under the same conditions with the same data.

Behavioral biometrics for persistent single sign-on

Driven by the rapid development of ubiquitous and pervasive computing, personalized services and applications are deployed to support our lives. Accordingly, the number of interfaces and devices (smartphone, tablet computer, etc.) provided to access and consume these services is growing continuously. To simplify the complexity of managing many accounts with different credentials, Single Sign-On (SSO) solutions have been introduced. However, a single password for many accounts represents a single-point-of-failure. Furthermore, once initiated SSO session is a high potential risk when the working station is left unlocked and unattended. In this paper, we present a conception of a Persistent Single Sign-On (PSSO) for ubiquitous home environments by involving the capabilities of Behavioral Biometrics to check the identity of the user continuously in an unobtrusive manner.

Diurnal variation of ocular biometrics under natural and defocused conditions

It is well known that a broad range of ocular anatomical and physiological parameters undergo significant diurnal variation. However, the natural diurnal variations that occur in the length of the human eye (axial length) and their underlying causes have been less well studied. Improvements in optical methods for the measurement of ocular biometrics now allow more precise and comprehensive measurements of axial length to be performed than has previously been possible. Research from animal models also suggests a link between diurnal axial length variations and longer term myopic eye growth, and that retinal image defocus can disrupt these diurnal rhythms in axial length. This research programme has examined the diurnal variations in axial length in young normal eyes, the contributing components and the influence of optical stimuli on these changes. In the first experiment, the normal pattern and consistency of the diurnal variations in axial length were examined at 10 different times (5 measurements each day, at ~ 3-hour intervals from ~ 9 am to ~ 9 pm) over 2 consecutive days on 30 young adult subjects (15 myopes, 15 emmetropes). Additionally, variations in a range of other ocular biometric measurements such as choroidal thickness, intraocular pressure, and other ocular biometrics were also explored as potential factors that may be associated with the observed variations in axial length. To investigate the potential influence of refractive error on diurnal axial length variations, the differences in the magnitude and pattern of diurnal variations in axial length between the myopic and emmetropic subjects were examined. Axial length underwent significant diurnal variation that was consistently observed over the 2 consecutive days of measurements, with the longest axial length typically occurring during the day, and the shortest at night. Significant diurnal variations were also observed in choroidal thickness, IOP and other ocular biometrics (such as central corneal thickness, anterior chamber depth and vitreous chamber depth) of the eye. Diurnal variations in vitreous chamber depth, IOP (positive associations) and choroidal thickness (negative association) were all significantly correlated with the diurnal changes in axial length. Choroidal thickness was found to fluctuate approximately in antiphase to the axial length changes, with the average timing of the longest axial length coinciding with the thinnest choroid and vice versa. There were no significant differences in the ocular diurnal variations associated with refractive error. Given that the diurnal changes in axial length could be associated with the changes in the eye’s optical quality, whether the optical quality of the eye also undergoes diurnal variation in the same cohort of young adult myopes and emmetropes over 2 consecutive days was also examined. Significant diurnal variations were observed only in the best sphere refraction (power vector M) and in the spherical aberration of the eye over two consecutive days of testing. The changes in the eyes lower and higher order ocular optics were not significantly associated with the diurnal variations in axial length and the other measured ocular biometric parameters. No significant differences were observed in the magnitude and timing of diurnal variations in lower-order and higher-order optics associated with refractive error. Since the small natural fluctuations in the eye’s optical quality did not appear to be sufficient to influence the natural diurnal fluctuations in ocular biometric parameters, in the next experiment, the influence of monocular myopic defocus (+1.50 DS) upon the normal diurnal variations in axial length and choroidal thickness of young adult emmetropic human subjects (n=13) imposed over a 12 hour period was examined. A series of axial length and choroidal thickness measurements (collected at ~3 hourly intervals, with the first measurement at ~9 am and the final measurement at ~9 pm) were obtained over three consecutive days. The natural diurnal rhythms (Day 1, no defocus), diurnal rhythms with monocular myopic defocus (Day 2, +1.50 DS spectacle lens over the right eye), and the recovery from any defocus induced changes (Day 3, no defocus) were examined. Significant diurnal variations over the course of the day were observed in both axial length and choroidal thickness on each of the three measurement days. The introduction of monocular myopic defocus led to significant reductions in the mean amplitude of diurnal change, and phase shifts in the peak timing of the diurnal rhythms in axial length and choroidal thickness. These defocus induced changes were found to be transient in nature and returned to normal the day following removal of the defocus. To further investigate the influence of optical stimuli on human diurnal rhythms, in the final experiment, the influence of monocular hyperopic defocus on the normal diurnal rhythms in axial length and choroidal thickness was examined in young adult emmetropic subjects (n=15). Similar to the previous experiment, the natural diurnal rhythms (Day 1, no defocus), diurnal rhythms with monocular hyperopic defocus (Day 2, -2.00 DS spectacle lens over the right eye), and the recovery from any defocus induced changes (Day 3, no defocus) were examined over three consecutive days. Both axial length and choroidal thickness underwent significant diurnal variations on each of the three days. The introduction of monocular hyperopic defocus resulted in a significant increase in the amplitude of diurnal change, but no change in the peak timing of diurnal rhythms in both parameters. The ocular changes associated with hyperopic defocus returned to normal, the day following removal of the defocus. This research has shown that axial length undergoes significant diurnal variation in young adult human eyes, and has shown that the natural diurnal variations in choroidal thickness and IOP are significantly associated, and may underlie these diurnal fluctuations in axial length. This work also demonstrated for the first time that exposing young human eyes to monocular myopic and hyperopic defocus leads to a significant disruption in the normal diurnal rhythms of axial length and choroidal thickness. These changes in axial length with defocus may reflect underlying mechanisms in the human eye that are involved in the regulation of longer term eye growth.

Changes in ocular biometrics and refraction during near work in downward gaze over time

PURPOSE To investigate changes in the characteristics of the corneal optics, total optics, anterior biometrics and axial length of the eye during a near task, in downward gaze, over 10 min. METHODS Ten emmetropes (mean - 0.14 ± 0.24 DS) and 10 myopes (mean - 2.26 ± 1.42 DS) aged from 18 to 30 years were recruited. To measure ocular biometrics and corneal topography in downward gaze, an optical biometer (Lenstar LS900) and a rotating Scheimpflug camera (Pentacam HR) were inclined on a custom built, height and tilt adjustable table. The total optics of the eye were measured in downward gaze with binocular fixation using a modified Shack-Hartmann wavefront sensor. Initially, subjects performed a distance viewing task at primary gaze for 10 min to provide a "wash-out" period for prior visual tasks. A distance task (watching video at 6 m) in downward gaze (25°) and a near task (watching video on a portable LCD screen with 2.5 D accommodation demand) in primary gaze and 25°downward gaze were then carried out, each for 10 min in a randomized order. During measurements, in dichoptic view, a Maltese cross was fixated with the right (untested) eye and the instrument’s fixation target was fixated with the subject’s tested left eye. Immediately after (0 min), 5 and 10 min from the commencement of each trial, measurements of ocular parameters were acquired in downward gaze. RESULTS Axial length exhibited a significant increase with downward gaze and accommodation over time (p<0.05). The greatest axial elongation was observed in downward gaze with 2.5 D accommodation after 10 min (mean change from baseline 23±3 µm). Downward gaze also caused greater changes in anterior chamber depth (ACD) and lens thickness (LT) with accommodation (ACD mean change -163±12µm at 10 min; LT mean change 173±17 µm at 10 min) compared to primary gaze with accommodation (ACD mean change -138±12µm at 10 min; LT mean change 131±15 µm at 10 min). Both corneal power and total ocular power changed by a small but significant amount with downward gaze (p<0.05), resulting in a myopic shift (~0.10 D) in the spherical power of the eye compared with primary gaze. CONCLUSION The axial length, anterior biometrics and ocular refraction change significantly with accommodation in downward gaze as a function of time. These findings provide new insights into the optical and bio-mechanical changes of the eye during typical near tasks.

Multiview gait biometrics for human identity recognition

We propose a novel multiview fusion scheme for recognizing human identity based on gait biometric data. The gait biometric data is acquired from video surveillance datasets from multiple cameras. Experiments on publicly available CASIA dataset show the potential of proposed scheme based on fusion towards development and implementation of automatic identity recognition systems.

Data-driven impostor selection for T-norm score normalisation and the background dataset in SVM-based speaker verification

A data-driven background dataset refinement technique was recently proposed for SVM based speaker verification. This method selects a refined SVM background dataset from a set of candidate impostor examples after individually ranking examples by their relevance. This paper extends this technique to the refinement of the T-norm dataset for SVM-based speaker verification. The independent refinement of the background and T-norm datasets provides a means of investigating the sensitivity of SVM-based speaker verification performance to the selection of each of these datasets. Using refined datasets provided improvements of 13% in min. DCF and 9% in EER over the full set of impostor examples on the 2006 SRE corpus with the majority of these gains due to refinement of the T-norm dataset. Similar trends were observed for the unseen data of the NIST 2008 SRE.