&lt;title&gt;Toward understanding the limits of gait recognition&lt;/title&gt;

Liu, Zongyi; Malavé, Laura Helena; Osuntogun, A.; Sudhakar, Preksha; Sarkar, Sudeep

doi:10.1117/12.543107

Cited by 24 publications

(15 citation statements)

References 24 publications

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“…A later study used manual labeling [23] to gain insight into the relationship between recognition capability and silhouette quality. Silhouettes were created for one gait cycle for 71 subjects under four different conditions, (shoe type, surface, and time) and each pixel was also labeled according to body segment.…”

Section: Gait Databasesmentioning

confidence: 99%

Automatic Recognition by Gait

2006

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| Recognizing people by gait has a unique advantage over other biometrics: it has potential for use at a distance when other biometrics might be at too low a resolution, or might be obscured. The current state of the art can achieve over 90% identification rate under situations where the training and test data are captured under similar conditions, while recognition rates with change of clothing, shoe, surface, illumination, and pose usually decrease performance and are the subject of much of the current study.Recognition can be achieved on outdoor data with uncontrolled illumination and at a distance when other biometrics could not be used. We shall show how this position has been achieved, covering most approaches to recognition by gait and the databases on which performance has been evaluated. We shall describe the context of these approaches, show how recognition by gait can be achieved and how current limits on performance are understood. We shall describe results on the most popular database, showing how recognition can handle some of the covariates that can affect recognition. We shall also investigate the supporting literature for this research, since the notion that people can be recognized by gait has support not only in medicine and biomedicine, and also in literature and psychology and other areas. In this way, we shall show that this new biometric has capability and research and application potential in other domains.

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Section: Gait Databasesmentioning

confidence: 99%

Automatic Recognition by Gait

2006

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“…More details about the process and quality checks can be found in [28], [29], here we highlight some salient aspects. Up to 71 subjects from one of the two collection periods (May collection) were chosen for manual silhouette specification.…”

Section: Manual Silhouettesmentioning

confidence: 99%

Effect of Silhouette Quality on Hard Problems in Gait Recognition

Liu

Sarkar

2005

IEEE Trans. Syst., Man, Cybern. B

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Abstract-Gait as a behavioral biometric has been the subject of recent investigations. However, understanding the limits of gait-based recognition and the quantitative study of the factors effecting gait have been confounded by errors in the extracted silhouettes, upon which most recognition algorithms are based. To enable us to study this effect on a large population of subjects, we present a novel model based silhouette reconstruction strategy, based on a population based hidden Markov model (HMM), coupled with an eigen-stance model, to correct for common errors in silhouette detection arising from shadows and background subtraction. The model is trained and benchmarked using manually specified silhouettes for 71 subjects from the recently formulated HumanID Gait Challenge database. Unlike other essentially pixel-level silhouette cleaning methods, this method can remove shadows, especially between feet for the legs-apart stance, and remove parts due to any objects being carried, such as briefcase or a walking cane. After quantitatively establishing the improved quality of the silhouette over simple background subtraction, we show on the 122 subjects HumanID Gait Challenge Dataset and using two gait recognition algorithms that the observed poor performance of gait recognition for hard problems involving matching across factors such as surface, time, and shoe are not due to poor silhouette quality, beyond what is available from statistical background subtraction based methods.

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“…This silhouette database includes 70 subjects over one walking cycle of approximately 30 to 40 image frames [27]. This cycle was chosen to begin at the right heel strike phase of the walking cycle through to the next right heel strike.…”

Section: Training Of the Hmmmentioning

confidence: 99%

Outdoor recognition at a distance by fusing gait and face

Liu

Sarkar

2007

Image and Vision Computing

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We explore the possibility of using both face and gait in enhancing human recognition at a distance performance in outdoor conditions. Although the individual performance of gait and face based biometrics at a distance under outdoor illumination conditions, walking surface changes, and time variations are poor, we show that recognition performance is significantly enhanced by combination of face and gait. For gait, we present a new recognition scheme that relies on computing distances based on selected, discriminatory, gait stances. Given a gait sequence, covering multiple gait cycles, it identifies the salient stances using a population hidden Markov model (HMM). An averaged representation of the detected silhouettes for these stances are then built using eigenstance shape models. Similarity between two gait sequences is based on the similarities of these averaged representations of the salient stances. This gait recognition strategy, which essentially emphasizes shape over dynamics, significantly outperforms the HumanID Gait Challenge baseline algorithm. For face, which is a mature biometric for which many recognition algorithms exists, we chose the elastic bunch graph matching based face recognition method. This method was found to be the best in the FERET 2000 studies. On a gallery database of 70 individuals and two probe sets: one with 39 individuals taken on the same day and the other with 21 individuals taken at least 3 months apart, results indicate that although the verification rate at 1% false alarm rate of individual biometrics are low, their combination performs better. Specifically, for data taken on the same day, individual verification rates are 42% and 40% for face and gait, respectively, but is 73% for their combination. Similarly, for the data taken with at least 3 months apart, the verification rates are 48% and 25% for face and gait, respectively, but is 60% for their combination. We also find that the combination of outdoor gait and one outdoor face per person is superior to using two outdoor face probes per person or using two gait probes per person, which can considered to be statistical controls for showing improvement by biometric fusion.

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<title>Toward understanding the limits of gait recognition</title>

Cited by 24 publications

References 24 publications

Automatic Recognition by Gait

Automatic Recognition by Gait

Effect of Silhouette Quality on Hard Problems in Gait Recognition

Outdoor recognition at a distance by fusing gait and face

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