Template-based gait authentication through Bayesian thresholding
TL;DR: This article proposes a method that uses the posterior probability of a Bayes ʼ classifier in place of the Euclidean distance and demonstrates that the Bayesian posterior probability performs significantly better than the de facto Euclideans distance approach and the cosine distance.
Abstract: While gait recognition is the mapping of a gait sequence to an identity known to the system, gait authentication refers to the problem of identifying whether a given gait sequence belongs to the claimed identity. A typical gait authentication system starts with a feature representation such as a gait template, then proceeds to extract its features, and a transformation is ultimately applied to obtain a discriminant feature set. Almost every authentication approach in literature favours the use of Euclidean distance as a threshold to mark the boundary between a legitimate subject and an impostor. This article proposes a method that uses the posterior probability of a Bayes ʼ classifier in place of the Euclidean distance. The proposed framework is applied to template-based gait feature representations and is evaluated using the standard CASIA-B gait database. Our study experimentally demonstrates that the Bayesian posterior probability performs significantly better than the de facto Euclidean distance approach and the cosine distance which is established in research to be the current state of the art.
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TL;DR: In this paper , a support vector machine (SVM) and a histogram of oriented gradients (HOG) were applied to classify images of the human gait in order to meet the objectives.
Abstract: Gait recognition provides the opportunity to identify different walking styles of people without physical intervention. However, covariates such as changing clothes and carrying conditions may influence recognition accuracy. Our objective was to identify the walking patterns of people for different covariates through analyzing images from publicly available data set CASIA-B on gait. On the dataset, the proposed method was evaluated by using GEI (gait energy image) as inputs for normal walking, changing clothes, and carrying conditions in a multi-view environment. A support vector machine (SVM) and a histogram of oriented gradients (HOG) were applied to classify images of the human gait in order to meet the objectives. Observations show that, under consideration of the mean of the individual accuracies, the accuracy of recognition is in the following order: clothing > normal walk > carrying at a 90° angle. Measurement accuracy of 87.9% was achieved for the coat-wearing people and measurement accuracy of 83.33% was achieved for all the mentioned covariates. The accuracy of the clothing covariate stated as 87.9% is a useful for people especially for different season like winter.
16 citations
TL;DR: The results suggest that combining sit-to-stand and stand- to-sit movements provides sufficient information for accurate person identification and such information can be remotely acquired using Doppler radar measurements.
Abstract: This article demonstrates the identification of 10 persons with 99% accuracy achieved by combining micro-Doppler signatures of sit-to-stand and stand-to-sit movements. Data from these movements are measured using two radars installed above and behind the person. Images of Doppler spectrograms generated using the measured data are combined and input to a convolutional neural network. Experimental results show the significantly better accuracy of the proposed method compared with conventional methods that do not perform data combination. The accuracy of identifying 10 participants having similar ages and physical features was 96–99%, despite the relatively small training set (number of training samples: 50–90 Doppler radar images per person). These results suggest that combining sit-to-stand and stand-to-sit movements provides sufficient information for accurate person identification and such information can be remotely acquired using Doppler radar measurements.
7 citations
Cites background from "Template-based gait authentication ..."
...Again, however, gait authentication systems relying on cameras [10], [11] present privacy issues....
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20 Feb 2020
TL;DR: The obtained results will prove that both the horizontal and vertical directions of the velocities of both movements include information that can be used to identify individuals, and this information can be obtained with micro-Doppler radar systems.
Abstract: This letter presents a method for person identification based on sit-to-stand and stand-to-sit movements using micro-Doppler radar measurements and a convolutional neural network (CNN). Two 24-GHz micro-Doppler radar systems placed directly above or behind participants will be used to measure the sit-to-stand and stand-to-sit movements of 10 participants. Images of the micro-Doppler signatures will be generated by subjecting the signals received by the radar to short-time Fourier transform. The generated images will then be used as input for the CNNs for training and evaluation purposes. The experiments verified the ability of the method to accurately identify people by measuring both their sit-to-stand and stand-to-sit movements. The identification accuracies for the sit-to-stand and stand-to-sit measurements were 93.6% and 94.9%, respectively, using the data of the radar placed above the participant, whereas the accuracy when placing the radar behind the participant was 92.9% for the sit-to-stand and 93.9% for the stand-to-sit movements. The obtained results will prove that both the horizontal and vertical directions of the velocities of both movements include information that can be used to identify individuals, and this information can be obtained with micro-Doppler radar systems.
4 citations
Cites background from "Template-based gait authentication ..."
...2975219 In many studies, the sensors used in authentication techniques based on the gait and other forms of behavior have been cameras [3], [4]....
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3 citations
Posted Content•
TL;DR: A novel end-to-end deep learning framework that utilizes the changes in orthogonal frequency division multiplexing (OFDM) sub-carrier amplitude information to simultaneously predict the identity, activity and the trajectory of a user and create a user profile that is of similar utility to a one made through a video camera based approach is introduced.
Abstract: Privacy issues related to video camera feeds have led to a growing need for suitable alternatives that provide functionalities such as user authentication, activity classification and tracking in a noninvasive manner. Existing infrastructure makes Wi-Fi a possible candidate, yet, utilizing traditional signal processing methods to extract information necessary to fully characterize an event by sensing weak ambient Wi-Fi signals is deemed to be challenging. This paper introduces a novel end to-end deep learning framework that simultaneously predicts the identity, activity and the location of a user to create user profiles similar to the information provided through a video camera. The system is fully autonomous and requires zero user intervention unlike systems that require user-initiated initialization, or a user held transmitting device to facilitate the prediction. The system can also predict the trajectory of the user by predicting the location of a user over consecutive time steps. The performance of the system is evaluated through experiments.
2 citations
Cites background from "Template-based gait authentication ..."
...Note that sub-carriers with indices [1, 2, 3, 4, 5, 6, 33, 60, 61, 62, 63, 64] have approximately zero amplitude in this case, and hence, highlighting the importance of the sparsity reduction proposed in Section III-B....
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...1) User Authentication: Majority of the wireless aided user authentication systems in the literature require the user to carry or wear a device to facilitate the authentication process [6]– [8]....
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References
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TL;DR: A new approach is proposed which combines canonical space transformation (CST) based on Canonical Analysis (CA), with EST for feature extraction, which can be used to reduce data dimensionality and to optimise the class separability of different gait classes simultaneously.
167 citations
"Template-based gait authentication ..." refers methods in this paper
...Principal component analysis (PCA) followed by linear discriminant analysis (LDA) produces a feature reduction technique which is sometimes referred to as canonical discriminant analysis (CDA) [1], [11], [22]....
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TL;DR: The ultimate goal of pattern recognition is to discriminate the class membership of the observed novel objects with the minimum misclassification rate.
Abstract: The ultimate goal of pattern recognition is to discriminate the class membership of the observed novel objects with the minimum misclassification rate. An observed object is often represented by a high dimensional real-valued vector after some preprocessing while its class membership can be represented by a much lower dimensional binary vector. Thus, in the discriminating process, a pattern recognition system intrinsically reduces the dimensionality of the input data into the number of classes.
163 citations
"Template-based gait authentication ..." refers methods in this paper
...The analysis of PCA + LDA is detailed in [23]....
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TL;DR: An algorithm is presented that provides the one-dimensional subspace, where the Bayes error is minimized for the C class problem with homoscedastic Gaussian distributions, and can be used to improve upon the outcomes provided by existing algorithms and derive a low-computational cost, linear approximation.
Abstract: We present an algorithm that provides the one-dimensional subspace, where the Bayes error is minimized for the C class problem with homoscedastic Gaussian distributions. Our main result shows that the set of possible one-dimensional spaces v, for which the order of the projected class means is identical, defines a convex region with associated convex Bayes error function g(v). This allows for the minimization of the error function using standard convex optimization algorithms. Our algorithm is then extended to the minimization of the Bayes error in the more general case of heteroscedastic distributions. This is done by means of an appropriate kernel mapping function. This result is further extended to obtain the d dimensional solution for any given d by iteratively applying our algorithm to the null space of the (d - l)-dimensional solution. We also show how this result can be used to improve upon the outcomes provided by existing algorithms and derive a low-computational cost, linear approximation. Extensive experimental validations are provided to demonstrate the use of these algorithms in classification, data analysis and visualization.
141 citations
"Template-based gait authentication ..." refers methods in this paper
...For instance, the result of LDA after PCA (i.e., CDA) reached up to 99 discriminant features for each gait template in our experimentation....
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...An illustration of the LDA mapping is shown in Fig....
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...The principle behind LDA is to produce a transformation so as to minimize the intra-class variance and maximize inter-class variance....
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...The analysis of PCA + LDA is detailed in [23]....
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...The Bayes’ classifier works efficiently with LDA [25], which is the dominant component of CDA....
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TL;DR: It is shown for the first time that elapsed time does not affect recognition significantly in the short-medium term, and this finding challenges the existing view in the literature that time significantly affects gait recognition.
Abstract: Many studies have shown that it is possible to recognize people by the way they walk. However, there are a number of covariate factors that affect recognition performance. The time between capturing the gallery and the probe has been reported to affect recognition the most. To date, no study has isolated the effect of time, irrespective of other covariates. Here, we present the first principled study that examines the effect of elapsed time on gait recognition. Using empirical evidence we show for the first time that elapsed time does not affect recognition significantly in the short-medium term. This finding challenges the existing view in the literature that time significantly affects gait recognition. We employ existing gait representations on a novel dataset captured specifically for this study. By controlling the clothing worn by the subjects and the environment, a Correct Classification Rate (CCR) of 95% has been achieved over the longest time period yet considered for gait on the largest ever temporal dataset. Our results show that gait can be used as a reliable biometric over time and at a distance if we were able to control all other factors such as clothing, footwear etc. We have also investigated the effect of different type of clothes, variations in speed and footwear on the recognition performance. The purpose of these experiments is to provide an indication of why previous studies (employing the same techniques as this study) have achieved significantly lower recognition performance over time. Our experimental results show that clothing and other covariates have been confused with elapsed time previously in the literature. We have demonstrated that clothing drastically affects the recognition performance regardless of elapsed time and significantly more than any of the other covariates that we have considered here.
107 citations
"Template-based gait authentication ..." refers methods in this paper
...[15] used the GEI and GEnI templates as features along the frontal, sagittal, and transverse planes for authentication....
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TL;DR: A method to select the most discriminative human body part based on group Lasso of motion to reduce the intra-class variation so as to improve the recognition performance is proposed.
Abstract: Gait recognition is an emerging biometric technology that identifies people through the analysis of the way they walk. The challenge of model-free based gait recognition is to cope with various intra-class variations such as clothing variations, carrying conditions and angle variations that adversely affect the recognition performance. This paper proposes a method to select the most discriminative human body part based on group Lasso of motion to reduce the intra-class variation so as to improve the recognition performance. The proposed method is evaluated using CASIA Gait Dataset B. Experimental results demonstrate that the proposed technique gives promising results.
96 citations
"Template-based gait authentication ..." refers methods in this paper
...Principal component analysis (PCA) followed by linear discriminant analysis (LDA) produces a feature reduction technique which is sometimes referred to as canonical discriminant analysis (CDA) [1], [11], [22]....
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...[11] obtained a better performance by performing body part segmentation on the GEI by applying group lasso of motion....
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