Analysis of time domain information for footstep recognition

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-17289-2_47Proceedings of 6th International Symposium, ISVC 2010, Las Vegas, NV, (USA)This paper reports an experimental analysis of footsteps as a biometric. The focus here is on information extracted from the time domain of signals collected from an array of piezoelectric sensors. Results are related to the largest footstep database collected to date, with almost 20,000 valid footstep signals and more than 120 persons, which is well beyond previous related databases. Three feature approaches have been extracted, the popular ground reaction force (GRF), the spatial average and the upper and lower contours of the pressure signals. Experimental work is based on a verification mode with a holistic approach based on PCA and SVM, achieving results in the range of 5 to 15% EER depending on the experimental conditions of quantity of data used in the reference models.R.V.-R., J.F. and J.O.-G. are supported by projects Contexts (S2009/TIC-1485), Bio-Challenge (TEC2009-11186), TeraSense (CSD2008-00068) and "Cátedra UAM-Telefónica"

A Comparative Study on Denoising Algorithms for Footsteps Sounds as Biometric in Noisy Environments

Biometrics is the automated identification of a person based on distinctive characteristics, such as fingerprints, face, voice, or the sound of footsteps. This last characteristic has significant challenges considering the background noise present in any real-life application, where microphones would record footsteps sounds and different types of noise. For this reason, it is crucial to consider not only the capacity of classification algorithms for recognizing a person using foostetps sounds, but also at least one stage of denoising algorithms that can reduce the background sounds before the classification. In this paper we study the possibilities of a two-stage approach for this problem: a denoising stage followed by a classification process. The work focuses on discovering the proper strategy for applying combinations of both stages for specific noise types and levels. Results vary according to the type and level of noise, e.g., for White noise at signal-to-noise ratio level, accuracy can increase from 0.96 to 1.00 by applying deep learning based-filters, but the same option does not benefit the cases of signals with low level natural noises, where Wiener filtering can increase accuracy from 0.6 to 0.77 at the highest level of noise. The results represent a baseline for developing real-life implementations of footstep biometrics.Universidad de Costa Rica/322–B9-105/UCR/Costa RicaUCR::Vicerrectoría de Docencia::Ingeniería::Facultad de Ingeniería::Escuela de Ingeniería Eléctric

Designing Intruder Detection System for Intelligent Responsive Safe Environment

To meet the ever growing need of creating an intelligent and responsive environment and also to provide safety and security to citizens, designing effective intruder detection system has become inevitable. During last few decades various technologies have been developed for intruder detection system but due to various associated issues like less accuracy, high cost, difficult implementability and others have kept the hunt on for much better and advanced system. So, under present research, technology and hardware for an indigenous intruder detection system based on ground vibration has been developed with geophone sensor. Through various trial and errors, a Ground Vibration Sensor System for Intruder Detection (GVSSID) has been successfully designed, implemented and tested at hardware level. Present GVSSID has shown a detection circle of radius three to four meters with maximum recorded output of more than 2 volts. The result analysis with present system has shown 80% accuracy in human footsteps detection. Result analysis of GVSSID has also shown that it is also possible to develop accurate human identification algorithm which is currently in progress under present research

Comparative analysis and fusion of spatiotemporal information for footstep recognition

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. R. Vera-Rodriguez, J. S. D. Mason, J. Fierrez, and J. Ortega-Garcia, "Comparative analysis and fusion of spatiotemporal information for footstep recognition", Pattern Analysis and Machine Intelligence, IEEE Transaction, vol. 35, no. 4, pp. 823-834, August 2012Footstep recognition is a relatively new biometric which aims to discriminate people using walking characteristics extracted from floor-based sensors. This paper reports for the first time a comparative assessment of the spatiotemporal information contained in the footstep signals for person recognition. Experiments are carried out on the largest footstep database collected to date, with almost 20,000 valid footstep signals and more than 120 people. Results show very similar performance for both spatial and temporal approaches (5 to 15 percent EER depending on the experimental setup), and a significant improvement is achieved for their fusion (2.5 to 10 percent EER). The assessment protocol is focused on the influence of the quantity of data used in the reference models, which serves to simulate conditions of different potential applications such as smart homes or security access scenarios.Ruben Vera-Rodriguez, Julian Fierrez and Javier Ortega Garcia are supported by projects Contexts (S2009/TIC-1485), Bio-Challenge (TEC2009-11186), TeraSense (CSD2008-00068) and ‘Catedra UAM-Telefonica’

Localisation of humans, objects and robots interacting on load-sensing floors

International audienceLocalisation, tracking and recognition of objects and humans are basic tasks that are of high value in applications of ambient intelligence. Sensing floors were introduced to address these tasks in a non-intrusive way. To recognize the humans moving on the floor, they are usually first localized, and then a set of gait features are extracted (stride length, cadence, pressure profile over a footstep). However, recognition generally fails when several people stand or walk together, preventing successful tracking. This paper presents a detection, tracking and recognition technique which uses objects' weight. It continues working even when tracking individual persons becomes impossible. Inspired by computer vision, this technique processes the floor pressure-image by segmenting the blobs containing objects, tracking them, and recognizing their contents through a mix of inference and combinatorial search. The result lists the probabilities of assignments of known objects to observed blobs. The concept was successfully evaluated in daily life activity scenarii, involving multi-object tracking and recognition on low resolution sensors, crossing of user trajectories, and weight ambiguity. This technique can be used to provide a probabilistic input for multi-modal object tracking and recognition systems

Analysis of Time Domain Information for Footstep Recognition

Abstract. This paper reports an experimental analysis of footsteps as a biometric. The focus here is on information extracted from the time domain of signals collected from an array of piezoelectric sensors. Results are related to the largest footstep database collected to date, with almost 20,000 valid footstep signals and more than 120 persons, which is well beyond previous related databases. Three feature approaches have been extracted, the popular ground reaction force (GRF), the spatial average and the upper and lower contours of the pressure signals. Experimental workisbasedonaverificationmodewithaholisticapproachbasedon PCA and SVM, achieving results in the range of 5 to 15 % EER depending on the experimental conditions of quantity of data used in the reference models.