Comparative Analysis and Fusion of Spatiotemporal Information for Footstep Recognition

Vera-Rodríguez, Rubén; Mason, John S.; Fiérrez, Julián; Ortega‐Garcia, Javier

doi:10.1109/tpami.2012.164

Cited by 90 publications

(76 citation statements)

References 26 publications

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“…Spatio-temporal raw and processed footstep data representations are designed and evaluated on deep machine learning models, by using the SFootBD database [11], the largest footstep database to date with more than 120 people and almost 20,000 footstep signals acquired from two rectangular arrays of 88 piezoelectric sensors each, to test the performance of our biometric system. Our deep machine learning models are based on the state-of-the-art resnet architecture [16] and the spatio-temporal two-stream architecture [17] [18] illustrated in Figure 2.…”

Section: Aims and Objectivesmentioning

confidence: 99%

“…This approach has the disadvantage of being highly vulnerable to noisy environmental conditions, such as illumination and cross-view [5]. An effective alternative to video stream data is biometric identification and verification from floor sensor systems [6], [7], [8], [9], [10], [11], [12], [13]. Footstep recognition uses the ground reaction force (GRF) induced by a client's footsteps on a floor sensor system to construct biometric systems for client identification or verification.…”

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confidence: 99%

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Analysis of Spatio-Temporal Representations for Robust Footstep Recognition with Deep Residual Neural Networks

Costilla-Reyes

Vera-Rodríguez

Scully

et al. 2019

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-Human footsteps can provide a unique behavioural pattern for robust biometric systems. We propose spatio-temporal footstep representations from floor-only sensor data in advanced computational models for automatic biometric verification. Our models deliver an artificial intelligence capable of effectively differentiating the fine-grained variability of footsteps between legitimate users (clients) and impostor users of the biometric system. The methodology is validated in the largest to date footstep database, containing nearly 20,000 footstep signals from more than 120 users. The database is organized by considering a large cohort of impostors and a small set of clients to verify the reliability of biometric systems. We provide experimental results in 3 critical data-driven security scenarios, according to the amount of footstep data made available for model training: at airports security checkpoints (smallest training set), workspace environments (medium training set) and home environments (largest training set). We report state-of-the-art footstep recognition rates with an optimal equal false acceptance and false rejection rate of 0.7% (equal error rate), an improvement ratio of 371% from previous state-of-the-art. We perform a feature analysis of deep residual neural networks showing effective clustering of clients footstep data and provide insights of the feature learning process.

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Section: Aims and Objectivesmentioning

confidence: 99%

mentioning

confidence: 99%

Analysis of Spatio-Temporal Representations for Robust Footstep Recognition with Deep Residual Neural Networks

Costilla-Reyes

Vera-Rodríguez

Scully

et al. 2019

IEEE Trans. Pattern Anal. Mach. Intell.

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“…Such is the case of the work in [16], where they fused information of the gait biometric trait with some shape cues such as body weight, width and some body part proportions. Then in [17,18], a multimodal system based on footsteps and gait was built. Likewise, in [19], a spatial temporal analysis of the lower part of the human silhouette was used to build a gait recognition system.…”

Section: Related Work On Shape-based Recognitionmentioning

confidence: 99%

Feature exploration for biometric recognition using millimetre wave body images

Gonzalez-Sosa¹,

Vera-Rodríguez²,

Fiérrez³

et al. 2015

J Image Video Proc.

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The use of millimetre wave images has been proposed recently in the biometric field to overcome certain limitations when using images acquired at visible frequencies. Furthermore, the security community has started using millimetre wave screening scanners in order to detect concealed objects. We believe we can exploit the use of these devices by incorporating biometric functionalities. This paper proposes a biometric recognition system based on the information of the silhouette of the human body, which may be seen as a type of soft biometric trait. To this aim, we report experimental results on the BIOGIGA database with four feature extraction approaches (contour coordinates, shape contexts, Fourier descriptors and landmarks) and three classification methods (Euclidean distance, dynamic time warping and support vector machines). The best configuration of 1.33 % EER is achieved when using contour coordinates with dynamic time warping.

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“…The first database used is the SFootBD [9]. This database is comprised of four biometric modes: footstep, gait, face and speech, using only the gait mode in this case.…”

Section: Gait Databasesmentioning

confidence: 99%

Analysis of Gait Recognition on Constrained Scenarios with Limited Data Information

Vera-Rodríguez

Gabriel-Sanz

Fiérrez

et al. 2013

Communications in Computer and Information Science

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El acceso a la versión del editor puede requerir la suscripción del recurso Access to the published version may require subscription Abstract. This paper is focused on the assessment of gait recognition on a constrained scenario, where limited information can be extracted from the gait image sequences. In particular we are interested in assessing the performance of gait images when only the lower part of the body is acquired by the camera and just half of a gait cycle is available (SFootBD database). Thus, various state-of-the-art feature approaches have been followed and applied to the data. Results show that good recognition performance can be achieved using such limited data information for gait biometric. A comparative analysis of the influence of the quantity of data used in the training models has been carried out obtaining results of 8.6% EER for the case of using 10 data samples to train the models, and 5.7% of EER for the case of using 40 data for training. Also, a comparison with a standard and ideal gait database (USF database) is also carried out using similar experimental protocols. In this case 10 data samples are used for training achieving results of 3.6% EER. The comparison with a standard database shows that different feature approaches perform differently for each database, achieving best individual results with MPCA and EGEI methods for the SFootBD and the USF databases respectively. Analysis of Gait Recognition on Constrained Scenarios with Limited Data Information

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Comparative Analysis and Fusion of Spatiotemporal Information for Footstep Recognition

Cited by 90 publications

References 26 publications

Analysis of Spatio-Temporal Representations for Robust Footstep Recognition with Deep Residual Neural Networks

Analysis of Spatio-Temporal Representations for Robust Footstep Recognition with Deep Residual Neural Networks

Feature exploration for biometric recognition using millimetre wave body images

Analysis of Gait Recognition on Constrained Scenarios with Limited Data Information

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