Application of the computational theory of perceptions to human gait pattern recognition

Triviño, Gracián; Alvarez-Alvarez, Alberto; Bailador, Gonzalo

doi:10.1016/j.patcog.2010.01.017

Cited by 58 publications

(35 citation statements)

References 17 publications

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“…These algorithms are: Active Energy Image (AEI) [8], Multilinear Principal Component Analysis (MPCA) [9], Gait Flow Image (GFI) [3], Gait Energy Image (GEI) [10], Motion Silhouette Contour Template (MSCT) [3] and Enhance Gait Energy Image (EGEI) [11].…”

Section: Feature Extractionmentioning

confidence: 99%

“…This paper is focused on gait recognition under limited data conditions. Gait is a relatively new biometric which utilizes the manner of walking to recognize an individual [3]. Compared to biometrics such as the iris or fingerprint recognition, this technique presents two main advantages: the recognition is performed at a distance and there is no need to cooperation from the users [4].…”

Section: Introductionmentioning

confidence: 99%

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Assessment of gait recognition based on the lower part of the human body

Gabriel-Sanz

Vera-Rodríguez

Tomé

et al. 2013

2013 International Workshop on Biometrics and Forensics (IWBF)

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Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in: ABSTRACTThis 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. A comparison with a standard and ideal gait database (USF database) is also carried out using similar experimental protocols. Results show that good recognition performance can be achieved using such limited data information for gait biometric (around 85% of rank 5 identification rate and 8.6% of 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 database respectively.

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Section: Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessment of gait recognition based on the lower part of the human body

Gabriel-Sanz

Vera-Rodríguez

Tomé

et al. 2013

2013 International Workshop on Biometrics and Forensics (IWBF)

View full text Add to dashboard Cite

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“…Trivino et al [16] used a linguistic model based on the computational theory of perceptions. The perception of the signal was modeled by a Fuzzy Finite State Machine (FFSM) and the model was expressed via linguistic terms.…”

Section: Introductionmentioning

confidence: 99%

Smartphone User Identity Verification Using Gait Characteristics

et al. 2016

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Smartphone-based biometrics offers a wide range of possible solutions, which could be used to authenticate users and thus to provide an extra level of security and theft prevention. We propose a method for positive identification of smartphone user's identity using user's gait characteristics captured by embedded smartphone sensors (gyroscopes, accelerometers). The method is based on the application of the Random Projections method for feature dimensionality reduction to just two dimensions. Then, a probability distribution function (PDF) of derived features is calculated, which is compared against known user PDF. The Jaccard distance is used to evaluate distance between two distributions, and the decision is taken based on thresholding. The results for subject recognition are at an acceptable level: we have achieved a grand mean Equal Error Rate (ERR) for subject identification of 5.7% (using the USC-HAD dataset). Our findings represent a step towards improving the performance of gait-based user identity verification technologies.

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“…Because the human gait is a periodic motion, there are several approaches for gait-based owner recognition: gait model-based, frequency analysis-based, and period detection-based approaches. In the model-based methods [27], [28], gait signal is modeled by using a finite state machine then some characteristics such as the gait period symmetry and homogeneity are extracted as gait pattern. In the frequency analysis-based methods, a histogram of signal intensity [7], [29] or a vector of coefficients of Fourier, wavelet transform [19], [30] are used as a gait pattern.…”

Section: Introductionmentioning

confidence: 99%

Orientation-Compensative Signal Registration for Owner Authentication Using an Accelerometer

Ngo

Makihara

Nagahara

et al. 2014

IEICE Trans. Inf. & Syst.

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SUMMARYGait-based owner authentication using accelerometers has recently been extensively studied owing to the development of wearable electronic devices. An actual gait signal is always subject to change due to many factors including variation of sensor attachment. In this research, we tackle to the practical sensor-orientation inconsistency, for which signal sequences are captured at different sensor orientations. We present an iterative signal matching algorithm based on phase-registration technique to simultaneously estimate relative sensor-orientation and register the 3D acceleration signals. The iterative framework is initialized by using 1D orientation-invariant resultant signals which are computed from 3D signals. As a result, the matching algorithm is robust to any initial sensororientation. This matching algorithm is used to match a probe and a gallery signals in the proposed owner authentication method. Experiments using actual gait signals under various conditions such as different days, sensors, weights being carried, and sensor orientations show that our authentication method achieves positive results.

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Application of the computational theory of perceptions to human gait pattern recognition

Cited by 58 publications

References 17 publications

Assessment of gait recognition based on the lower part of the human body

Assessment of gait recognition based on the lower part of the human body

Smartphone User Identity Verification Using Gait Characteristics

Orientation-Compensative Signal Registration for Owner Authentication Using an Accelerometer

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