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Gait is not actually a new biometric[1,2]. There was a group of concurrent workers in the mid 1990s who showed independently and on small databases that gait could be used to identify people, by the way they walk. The current state-of-the-art systems have demonstrated recognition capability on much larger databases where subjects were walking indoors in controlled lighting, and outdoors where the lighting is uncontrolled, and where other biometrics are at too low a resolution or are obscured.

There is quite a rich stock of support for gait as a biometric. Shakespeare notes “Great Juno comes I do know her by her gait” (The Tempest, Act 4). Biomechanical studies suggest that our gait is unique to us; from research starting in the 1970s, psychologists have noted that people can recog-nise human movement, then friends and gender. Gait has also been used for forensic purposes, to identify the perpetrator. In a recent burglary in the UK, a suspect’s face could not be perceived but his unique gait was used to aid identification in near scene-of-crime camera footage.

Essentially, we use computer vision tech-niques to deploy gait as a biometric: we take a sequence of images and derive from them a set of numbers which are unique to an individual. There are two main ways to derive the set of numbers: in model-based approaches the set of numbers reflects move-ment of the human limbs; in silhouette-based approaches the numbers are derived from a sequence of silhouettes, thereby describing body shape and movement concurrently. This is illustrated in Figure 1, where an image from a sequence of a walking subject (from the Southampton gait database, summarised in Table 1) is processed first to derive the sil-houette, and from this numbers are extracted which reflect the subject’s identity. Clusters of measurements are labelled with a different colour for different subjects – here, the sepa-ration between the clusters of measurements shows that recognition can be achieved.

Many of the techniques are similar to those used in other biometrics: feature extraction techniques are used to derive the features on which analysis is based; feature set selection determines the potency of the measures used; and classification approaches associate identity with the data provided. This is then similar to pattern recognition in general: if we can derive a set of measures for which the within-class (the intra-subject) variability is less than the between-class (the inter-subject) variability, then recognition can be achieved. This has been demonstrated to be achievable for fingerprints, for faces, and now for gait.

There is a world-wide research in the sub-ject. Initial impetus was reinforced by the Defense Advanced Projects Research Agency’s

(DARPA’s) Human ID at a Distance pro-gramme where researchers from Maryland, MIT, Carnegie Mellon, South Florida, Georgia Tech and Southampton developed a series of approaches and databases which showed that gait could be deployed for biometric purposes. Later research has developed, particularly in China, US and the UK, to confirm these original results.

A metastudy of performance achieve-ment is given in Table 2. The performance for all these studies was evaluated using the Southampton dataset. The approaches show that in this research phase, high recognition rates can indeed be achieved. The sites which are conducting gait research are world-wide and the publishing venues are those common for biometrics research. There is quite a wide variety of techniques, and most of them are based on the motion of the human silhouette; few approaches are model-based, using the motion of the limbs.

Our newer research will be published soon, at the IEEE Conference on Face and Gesture Recognition 2008. There we will show how gait changes, with different load, walking speed, shoes and clothing[3]. We will also describe our new approach which uses gait as a self-calibrating approach, wherein we can derive a gait recogni-tion signature from a single camera, with an arbitrary viewpoint[4]. The approach can derive a viewpoint-invariant signature under the assump-tions that we have a rigid body structure and walk in an approximately straight line for two gait cycles, by determining the motion of the joints in human legs. Other research continues to investigate alternative recognition approaches, potency of the measures used, fusion with other biometrics, alternative sensing modalities and how invariant-gait signatures can be derived.

The research continues and, as in any subject, the remit widens. This is natural since

Gait biometricsGiven the plethora of biometrics that are currently available, many will ask whether we need a new one. If the new biometric has a unique advantage and it can contribute to the fusion with other biometrics, the answer is a clear yes.

Figure 1: Gait Recognition by Silhouette Analysis:(a) video data, (b) silhouette and (c) feature space.

Camera Scan Type View Angle # subjects Locality Walk Surface

A Progressive scan Normal 116 Indoors Track

D Interlaced Oblique 116 Indoors Track

B Progressive scan Normal 116 Indoors Treadmill

C Interlaced Oblique 116 Indoors Treadmill

E Progressive scan Normal 116 Outdoors Track

F Interlaced Oblique 116 Outdoors TrackTable 1: Southampton Large-Subject Gait Databases[5] recorded 2000-2002, available via http://www.gait.ecs.soton.ac.uk . There are about 16 sequences of 116 subjects recorded using DV camcorders. The lighting indoors is controlled with a chromakey background; outside the lighting and background are uncontrolled. Each sequence is of one gait cycle (two strides) of around thirty 720×576 images. The databases most generally used for evaluation are A and E. The other main databases are the NIST HiD database (USA), and the CASIA databases (China).

(a)

(b)

(c)

Biometric Technology Today July/August 2008

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FEATURE

walking is a natural human activity, so there are putative extensions in medicine, sports and graphics analysis. There is natural emergent interest in deploying gait in surveillance sce-narios: the unique advantage of gait is that it is available at a distance when other biometrics are at too low a resolution, or intentionally hidden, as commonly observed in scene-of-crime footage. There is natural interest in the factors that affect walking patterns; gait is a behavioural biometric that can be affected by clothing or by footwear. This is actually of advantage since there is potential to investigate the effects of carried load (especially when it is concealed), or even the masquerade as a subject of different gender (both of these have been observed with suicide bombers), and analysing gait gives the power to analyse behaviour, say at airports.

Our own view is that in some ways the sugges-tion that gait is unique is still found to be new to the biometrics community. As such, there is still room for research which capitalises on the benefits when using gait alone and when used in tandem, or fusion, with other biometrics. Clearly, using gait can cue the enrolment of other biometrics, or the location of pedestrians in outdoor scenes. As a biometric, it requires people to walk and is thus ideally suited for an entrance portal or biometrics gateway. Other biometrics portals

include one by Sarnoff (USA) using iris (and face) as a subject walks through the system, but not gait. We have developed a portal[6] which uses face, gait and ear (Fig. 2) and are deploying it to capture a large database of subjects’ ear, face and gait for biometric evaluation. For use in surveil-lance, there have already been convictions where the identity was confirmed by the suspect’s gait. For use of automated techniques in surveillance, we need viewpoint-invariant approaches that can be deployed using a single camera, and that has indeed been our aim. Others have studied the effects of viewpoint on recognition procedures, giving an alternative approach.

Overall, research has demonstrated that it is possible to recognise people by the way they walk. Research continues to improve the anal-ysis and the stock of techniques that can be used for these purposes. Since walking is a nat-ural human activity there is wider interest in the analysis of gait. As such we look forward to the future developments in this interesting and challenging subject, and to commercial development to exploit this new field.

This feature was provided by Professor Mark Nixon, School of Electronics and Computer Science, University of Southampton, UK. He can be contacted on Tel: +44 23 8059 3542, Email: msn@ecs.soton.ac.uk, Web: www.ecs.soton.ac.uk/~msn

1st Author From Published Which Database? CCR Method

Veres Southampton UK CVPR 2004 Indoor A 96 Silhouette measures, including potency

Wagg Southampton UK FG 2004 Indoor A/Outdoor E 85/64 Model-based

Boyd Calgary, Canada CVIU 2004 Indoor A 88 Silhouette

Yu Beijing, China I&G 2004 Indoor A 85 Silhouette perimeter

Lam Hong Kong, China PR 2007 Indoor A 90 Motion silhouette

Lee Rutgers, USA FG 2006 Indoor A 84 Dynamic shapeTable 2: Results on Southampton Databases. Derived from top Google Scholar cited papers which themselves cite and evaluate the Southampton database in [5], and including only the two top-cited papers by Southampton researchers. The CCR is the Correct Classification rate usually given for the best match. Many papers give the CCR on the first 10 matches (the Cumulative Match Characteristic CMC) which is 100% when given.

Figure 2: Biometrics Tunnel.

(a) plan (b) reality

References [1] M. S. Nixon, T. N. Tan and R. Chellappa, Human Identification based on Gait, Springer, International Series on Biometrics (A. K. Jain and D. Zhang Eds.) 2005[2] M. S. Nixon and J. N. Carter, Human ID based on Gait, Proceedings of the IEEE 94(11), pp. 2013-2024, 2006[3] I. Bouchrika and M. S. Nixon, Exploratory Factor Analysis of Gait Recognition, to be presented at IEEE FG2008, Amsterdam, September 2008[4] M. Goffredo, R. D. Seely, J. N. Carter and M. S. Nixon, Markerless View Independent Gait Analysis with Self-camera Calibration, to be presented at IEEE FG2008, Amsterdam, September 2008[5] J. D. Shutler, M. G. Grant, M. S. Nixon, and J. N. Carter, On a Large Sequence-Based Human Gait Database, Proc. 4th International Conference on Recent Advances in Soft Computing, Nottingham (UK), pp 66-71, 2002[6] L. Middleton, D. K. Wagg, A. I. Bazin, J. N. Carter and M. S. Nixon, Developing a non-intrusive biometric environment. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Beijing, China, 2006

July/August 2008 Biometric Technology Today