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.
Citations
More filters
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....
[...]
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]....
[...]
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....
[...]
...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]....
[...]
References
More filters
TL;DR: A VTM incorporating a score normalization framework with quality measures that encode the degree of the bias is proposed, and the experimental results show that incorporating the quality measures contributes to accuracy improvement in many cross-view settings.
Abstract: Cross-view gait recognition authenticates a person using a pair of gait image sequences with different observation views. View difference causes degradation of gait recognition accuracy, and so several solutions have been proposed to suppress this degradation. One useful solution is to apply a view transformation model (VTM) that encodes a joint subspace of multiview gait features trained with auxiliary data from multiple training subjects, who are different from test subjects (recognition targets). In the VTM framework, a gait feature with a destination view is generated from that with a source view by estimating a vector on the trained joint subspace, and gait features with the same destination view are compared for recognition. Although this framework improves recognition accuracy as a whole, the fit of the VTM depends on a given gait feature pair, and causes an inhomogeneously biased dissimilarity score. Because it is well known that normalization of such inhomogeneously biased scores improves recognition accuracy in general, we therefore propose a VTM incorporating a score normalization framework with quality measures that encode the degree of the bias. From a pair of gait features, we calculate two quality measures, and use them to calculate the posterior probability that both gait features originate from the same subjects together with the biased dissimilarity score. The proposed method was evaluated against two gait datasets, a large population gait dataset of over-ground walking (course dataset) and a treadmill gait dataset. The experimental results show that incorporating the quality measures contributes to accuracy improvement in many cross-view settings.
94 citations
"Template-based gait authentication ..." refers methods in this paper
...A typical VTM applies affine transformation to the probe sequence to match the view angle to that of the stored gallery instance....
[...]
...The method can also be integrated to view estimators (such as in [12]) to enable a view-invariant authentication operation or any other VPM or VTM depending on the implementation design constraints....
[...]
...Then, the system can either apply a view transformation model (VTM) [29] or a view preserving model (VPM) [12] before the actual prediction process....
[...]
...Nevertheless, VTMs do have certain practical advantages over VPMs; VTM-based systems save space as only a single gallery angle and a corresponding model is required....
[...]
...Also, the training session is significantly simpler in VTM as only a single camera is required, whereas VPM requires several video instances of the subject per angle....
[...]
TL;DR: A novel system for gait recognition and verification based on the matching of linearly time-normalized gait walking cycles and a novel feature extraction process for the transformation of human silhouettes into low-dimensional feature vectors consisting of average pixel distances from the center of the silhouette are presented.
89 citations
"Template-based gait authentication ..." refers background in this paper
...synonymous in usage, such as in [2] and [3]....
[...]
...[3] surpassed this baseline performance with the use of angular...
[...]
TL;DR: A novel fuzzy color difference histogram (FCDH) is proposed by using fuzzy c-means (FCM) clustering and exploiting the CDH, which reduces the large dimensionality of the histogram bins in the computation and lessens the effect of intensity variation generated due to the fake motion or change in illumination of the background.
Abstract: Detection of moving objects in the presence of complex scenes such as dynamic background (e.g, swaying vegetation, ripples in water, spouting fountain), illumination variation, and camouflage is a very challenging task. In this context, we propose a robust background subtraction technique with three contributions. First, we present the use of color difference histogram (CDH) in the background subtraction algorithm. This is done by measuring the color difference between a pixel and its neighbors in a small local neighborhood. The use of CDH reduces the number of false errors due to the non-stationary background, illumination variation and camouflage. Secondly, the color difference is fuzzified with a Gaussian membership function. Finally, a novel fuzzy color difference histogram (FCDH) is proposed by using fuzzy c-means (FCM) clustering and exploiting the CDH. The use of FCM clustering algorithm in CDH reduces the large dimensionality of the histogram bins in the computation and also lessens the effect of intensity variation generated due to the fake motion or change in illumination of the background. The proposed algorithm is tested with various complex scenes of some benchmark publicly available video sequences. It exhibits better performance over the state-of-the-art background subtraction techniques available in the literature in terms of classification accuracy metrics like $MCC$ and $PCC$ .
77 citations
"Template-based gait authentication ..." refers background in this paper
...Dynamic backgrounds might require the use of specialized algorithms (such as in [19])....
[...]
TL;DR: The proposed panoramic gait recognition framework can greatly reduce the complexity of the classification problem while achieving fair correct classification rates when gait is captured with unknown conditions.
73 citations
"Template-based gait authentication ..." refers background or 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....
[...]
...The feature representation used, i.e., gait templates, and the application of CDA feature reduction are the same for both NN and BT for a fair comparison of performance....
[...]
...The Bayes’ classifier works efficiently with LDA [25], which is the dominant component of CDA....
[...]
...The nature of gait feature extraction can be either model-free or model-based [1]....
[...]
...[1] passed the GEI through a random forests classifier to find the most discriminating pixels that are resilient to covariate factors....
[...]
TL;DR: This paper presents gait image features based on the information set theory, henceforth these are called gait information image features, and demonstrates the robustness of the proposed features.
60 citations
"Template-based gait authentication ..." refers background in this paper
...synonymous in usage, such as in [2] and [3]....
[...]