Gait-based Person Identification Research Articles

MODELS AND METHODS FOR GAIT-BASED PERSON IDENTIFICATION WHILE WEARING DIFFERENT OUTFIT

Abstract. This study presents a potential methodology for the identification of individuals based on their gait patterns, even when they are wearing different attire. The approach described in this study utilizes modified local patterns that have been specifically designed to be robust to rotation. These patterns enable the extraction of more discerning characteristics, particularly in scenarios where individuals may be wearing diverse clothing. The procedure was outlined in a comprehensive manner, including explanations for each step of the process. The experiments provided confirmation of the notable efficiency of gait-based person identification in scenarios where individuals wear varying attire. Keywords: Surveillance systems, Human identification, Silhouette extraction, Gait Energy Image, classification, Local Binary Pattern, Gait recognition.

MODELS AND METHODS FOR GAIT-BASED PERSON IDENTIFICATION WHILE WEARING DIFFERENT OUTFIT

Abstract. This study presents a potential methodology for the identification of individuals based on their gait patterns, even when they are wearing different attire. The approach described in this study utilizes modified local patterns that have been specifically designed to be robust to rotation. These patterns enable the extraction of more discerning characteristics, particularly in scenarios where individuals may be wearing diverse clothing. The procedure was outlined in a comprehensive manner, including explanations for each step of the process. The experiments provided confirmation of the notable efficiency of gait-based person identification in scenarios where individuals wear varying attire. Keywords: Surveillance systems, Human identification, Silhouette extraction, Gait Energy Image, classification, Local Binary Pattern, Gait recognition.

Gait Based Person Identification Using Deep Learning Model of Generative Adversarial Network

The proliferation of digital age security tools is often attributed to the rise of visual surveillance. Since an individual's gait is highly indicative of their identity, it is becoming an increasingly popular biometric modality for use in autonomous visual surveillance and monitoring. There are various steps used in gait recognition frameworks such as segmentation, feature extraction, feature learning and similarity measurement. These steps are mutually independent with each part fixed, which results in a suboptimal performance in a challenging condition. It can be done independently of the users' involvement. Low-resolution video and straightforward instrumentation can verify an individual's identity, making impersonation a rarity. Using the benefits of the Generative Adversarial Network (GAN), this investigation tackles the problem of unevenly distributed unlabeled data with infrequently performed tasks. To estimate the data circulation in various circumstances using constrained observed gait data, a multimodal generator is applied here. When it comes to sharing knowledge, the variety provided by the data generated by a multimodal generator is hard to beat. The capability to distinguish gait activities with varying patterns due to environmental dynamics is enhanced by this multimodal generator. This system is more stable than other gait-based recognition methods because it can process data that is not equally dispersed throughout a different environment. The system's reliability is enhanced by the multimodal generator's capacity to produce a wide variety of outputs. The testing results show that this algorithm is superior to other gait-based recognition methods because it can adapt to changing environments.

Learning Gait Representations with Noisy Multi-Task Learning.

Gait analysis is proven to be a reliable way to perform person identification without relying on subject cooperation. Walking is a biometric that does not significantly change in short periods of time and can be regarded as unique to each person. So far, the study of gait analysis focused mostly on identification and demographics estimation, without considering many of the pedestrian attributes that appearance-based methods rely on. In this work, alongside gait-based person identification, we explore pedestrian attribute identification solely from movement patterns. We propose DenseGait, the largest dataset for pretraining gait analysis systems containing 217 K anonymized tracklets, annotated automatically with 42 appearance attributes. DenseGait is constructed by automatically processing video streams and offers the full array of gait covariates present in the real world. We make the dataset available to the research community. Additionally, we propose GaitFormer, a transformer-based model that after pretraining in a multi-task fashion on DenseGait, achieves 92.5% accuracy on CASIA-B and 85.33% on FVG, without utilizing any manually annotated data. This corresponds to a +14.2% and +9.67% accuracy increase compared to similar methods. Moreover, GaitFormer is able to accurately identify gender information and a multitude of appearance attributes utilizing only movement patterns. The code to reproduce the experiments is made publicly.

Unsupervised Domain Adaptation for Disguised-Gait-Based Person Identification on Micro-Doppler Signatures

In recent years, gait-based person identification has gained significant interest for a variety of applications, including security systems and public security forensics. Meanwhile, this task is faced with the challenge of disguised gaits. When a human subject changes what he or she is wearing or carrying, it becomes challenging to reliably identify the subject’s identity using gait data. In this paper, we propose an unsupervised domain adaptation (UDA) model, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Guided Subspace Alignment under the Class-aware</i> condition (G-SAC), to recognize human subjects based on their disguised gait data by fully exploiting the intrinsic information in gait biometrics. To accomplish this, we employ neighbourhood component analysis (NCA) to create an intrinsic feature subspace from which we can obtain similarities between normal and disguised gaits. With the aid of a proposed constraint for adaptive class-aware alignment, the class-level discriminative feature representation can be learned guided by this subspace. Our experimental results on a measured micro-Doppler radar dataset demonstrate the effectiveness of our approach. The comparison results with several state-of-the-art methods indicate that our work provides a promising domain adaptation solution for the concerned problem, even in cases where the disguised pattern differs significantly from the normal gaits. Additionally, we extend our approach to more complex multi-target domain adaptation (MTDA) challenge and video-based gait recognition tasks, the superior results demonstrate that the proposed model has a great deal of potential for tackling increasingly difficult problems.

Gait Identification Using Limb Joint Movement and Deep Machine Learning

Person identification is a key problem in the security domain and may be used to automatically identify criminals or missing persons. The traditional face matching approaches adopted by the police and security services across the world have recently been shown to produce a high rate of false positive identification. Alternatively, gait-based person identification has shown to be a convenient method particularly as it can be performed at a distance, without the cooperation of the subject, and is a biometric trait which cannot be easily disguised. In this work, we propose a gait-based person identification approach which uses limb joint motion data and deep machine learning models to identify the individuals. Distinct statistical features are identified and extracted from limb movement using a fixed width sliding window to train a Long Short-Term Memory model. The proposed solution outperforms the existing methods producing 98.87% accuracy when evaluated over unseen samples. In addition, we propose a simple two-stage filtering approach to increase the prediction accuracy up to 100% when identifying individuals from larger sequences of samples. This finding may improve the current solutions in controlled environments such as airports. In the future, this approach may help to overcome the problem of occlusion in gait-based identification, as unlike the existing works, it does not require information regarding the entire body. The study also presents a primary dataset comprising limb joint movement acquired from a diverse range of participants during casual walking captured through two digital goniometers.

Robust gait based human identification on incomplete and multi-view sequences

Gait based person identification is an important research area in the field of video surveillance. The major challenges faced by gait recognition systems in real-life scenarios include view variance, occlusion and resultant unavailability of a complete sequence containing a gait cycle. In this work, we propose a novel robust gait recognition framework capable of handling these challenges. We show how Gait-Energy-Images (GEIs) can be accurately constructed from largely incomplete input silhouette sequences. This provides an immediate advantage over current literature that assumes availability of complete sequences. We then highlight the shortcoming of most of the current view-invariant models that perform sub-optimal transformation of probe and gallery sequences captured in different views for comparison. We propose a model which jointly estimates and learns the optimal transformation for comparison of probe and gallery GEIs. Through extensive experiments, we show that our proposed framework is able to outperform most state-of-the-art methods on multiple benchmarks.

Gait-based person identification using 3D LiDAR and long short-term memory deep networks

Gait recognition is one measure of biometrics, which also includes facial, fingerprint, and retina recognition. Although most biometric methods require direct contact between a device and a subject, gait recognition has unique characteristics whereby interaction with the subjects is not required and can be performed from a distance. Cameras are commonly used for gait recognition, and a number of researchers have used depth information obtained using an RGB-D camera, such as the Microsoft Kinect. Although depth-based gait recognition has advantages, such as robustness against light conditions or appearance variations, there are also limitations. For instance, the RGB-D camera cannot be used outdoors and the measurement distance is limited to approximately 10 meters. The present paper describes a long short-term memory-based method for gait recognition using a real-time multi-line LiDAR. Very few studies have dealt with LiDAR-based gait recognition, and the present study is the first attempt that combines LiDAR data and long short-term memory for gait recognition and focuses on dealing with different appearances. We collect the first gait recognition dataset that consists of time-series range data for 30 people with clothing variations and show the effectiveness of the proposed approach.

Person identification from partial gait cycle using fully convolutional neural networks

Gait as a biometric property for person identification plays a key role in video surveillance and security applications. In gait recognition, normally, gait feature such as Gait Energy Image (GEI) is extracted from one full gait cycle. However in many circumstances, such a full gait cycle might not be available due to occlusion. Thus, the GEI is not complete giving rise to a degrading in gait-based person identification rate. In this paper, we address this issue by proposing a novel method to identify individuals from gait feature when a few (or even single) frame(s) is available. To do so, we propose a deep learning-based approach to transform incomplete GEI to the corresponding complete GEI obtained from a full gait cycle. More precisely, this transformation is done gradually by training several auto encoders independently and then combining these as a uniform model. Experimental results on two public gait datasets, namely OULP and Casia-B demonstrate the validity of the proposed method in dealing with very incomplete gait cycles.

Fusion of Random Walk and Discrete Fourier Spectrum Methods for Gait Recognition

Gait-based person identification suffers from the problem of different covariate factors such as clothing and carrying objects, which drastically reduce the recognition rate. Most existing methods capture dynamic and static information and remove the covariate factors without any systematic study. However, it has been reported in the literature that the head is one of the important features and the removal of the head from static information decreases the recognition rate. In our preliminary study, we developed a novel random walk (RW)-based gait extraction method that retains the head portion and removes certain static body parts to reduce the effect of covariate factors. The RW-based method is a novel gait feature extraction method and should be exploited more for its discriminative power to separate different body parts efficiently. However, the dynamic part is also significant in gait information, which is not very effectively represented in the RW-based gait extraction method. Therefore, a discrete Fourier transform (DFT)-based frequency component of the gait is considered to represent the dynamic part of gait information. Furthermore, we propose a novel gait recognition algorithm that fuses dynamic and static information from DFTand RW-based representations. The proposed method systematically retains the discriminative static gait information along with the frequency attribute embedded as the dynamic gait information. Extensive experiments on the Chinese Academy of Sciences, Institute of Automation and the HumanID datasets have been carried out to demonstrate that the proposed fused gait features-based approach outperforms the existing methods, particularly when there are substantial appearance changes.

Robust Gait Recognition by Integrating Inertial and RGBD Sensors.

Gait has been considered as a promising and unique biometric for person identification. Traditionally, gait data are collected using either color sensors, such as a CCD camera, depth sensors, such as a Microsoft Kinect, or inertial sensors, such as an accelerometer. However, a single type of sensors may only capture part of the dynamic gait features and make the gait recognition sensitive to complex covariate conditions, leading to fragile gait-based person identification systems. In this paper, we propose to combine all three types of sensors for gait data collection and gait recognition, which can be used for important identification applications, such as identity recognition to access a restricted building or area. We propose two new algorithms, namely EigenGait and TrajGait, to extract gait features from the inertial data and the RGBD (color and depth) data, respectively. Specifically, EigenGait extracts general gait dynamics from the accelerometer readings in the eigenspace and TrajGait extracts more detailed subdynamics by analyzing 3-D dense trajectories. Finally, both extracted features are fed into a supervised classifier for gait recognition and person identification. Experiments on 50 subjects, with comparisons to several other state-of-the-art gait-recognition approaches, show that the proposed approach can achieve higher recognition accuracy and robustness.

I-vector analysis for Gait-based Person Identification using smartphone inertial signals

This paper describes and evaluates an i-vector based approach for Gait-based Person Identification (GPI) using inertial signals from a smartphone. This approach includes two variability compensation strategies (Linear Discrimination Analysis (LDA) and Probabilistic LDA) for dealing with the gait variability due to different recording sessions or different activities carried out by the user. This study uses a public available dataset that includes recordings from 30 users performing three different activities: walking, walking-upstairs and walking-downstairs.The i-vector approach is compared to a Gaussian Mixture Model-Universal Background Model (GMM-UBM) system, providing significant performance improvements when incorporating the PLDA compensation strategy: the best result reports a User Recognition Error Rate (URER) of 17.7%, an Equal Error Rate (EER) of 6.1% and an F1-score of 82.7% with 30 enrolled users. For less than six enrolled users, the URER error decreases to 5%.

Frequency features and GMM-UBM approach for gait-based person identification using smartphone inertial signals

This paper describes the development of a Gait-based Person Identification (GPI) system based on a Gaussian Mixture Model-Universal Background Model (GMM-UBM) approach that uses inertial signals from a smartphone. The system integrates five main modules or steps: signal pre-processing, feature extraction, GMM-UBM training, Maximum A Posteriori (MAP) adaptation, and a comparison module for providing the identified user. This system also integrates new feature extraction strategies proposed recently (Mel Frequency Cepstral Coefficients (MFCCs) and Perceptual Lineal Prediction (PLP) coefficients) for improving the results. This study has been done using the public available dataset called UCI Human Activity Recognition Using Smartphones dataset. A six-fold cross-validation procedure has been carried out, showing the average value for every experiment. The final results demonstrate the capability of the GMM-UBM approach for gait recognition, and show how the PLP coefficients can improve system performance while reducing drastically the number of features (from 561 to 90). The best result shows a User Recognition Error Rate of 34.0% with 30 enrolled users. When reducing the number of enrolled users, the error rate decreases: for a number smaller than six, the URER error becomes lower than 10%.

相互部分空間法による歩行速度の変化に頑強な個人認証

Gait-based person identification from images is not robust to speed changes. We proposed the method which applies a mutual subspace method (MSM) to gait images under speed changes, based on the following idea. Since speed variations are universal phenomena, there is a possibility that speed information does not have to be regarded as critical information to gait recognition. This idea lead us to an idea that an image set-based matching approach can solve the gait recognition problem. The MSM-based method outperformed an existing method through experiments on OU-ISIR gait speed transition database. In this paper we apply the MSM-based method to different database of speed changes, OU-ISIR Treadmill Dataset A, to show the effectiveness of the method.

Gait-Based Person Identification Robust to Changes in Appearance

The identification of a person from gait images is generally sensitive to appearance changes, such as variations of clothes and belongings. One possibility to deal with this problem is to collect possible subjects' appearance changes in a database. However, it is almost impossible to predict all appearance changes in advance. In this paper, we propose a novel method, which allows robustly identifying people in spite of changes in appearance, without using a database of predicted appearance changes. In the proposed method, firstly, the human body image is divided into multiple areas, and features for each area are extracted. Next, a matching weight for each area is estimated based on the similarity between the extracted features and those in the database for standard clothes. Finally, the subject is identified by weighted integration of similarities in all areas. Experiments using the gait database CASIA show the best correct classification rate compared with conventional methods experiments.

Group Context-aware Person Identification in Video Sequences

The importance of person identification techniques is increasing for visual surveillance applications. In social living scenarios, people often act in groups composed of friends, family, and co-workers, and this is a useful cue for person identification. This paper describes a method for person identification in video sequences based on this group cue. In the proposed approach, the relationships between the people in an input sequence are modeled using a graphical model. The identity of each person is then propagated to their neighbors in the form of message passing in a graph via belief propagation, depending on each person's group affiliation information and their characteristics, such as spatial distance and velocity vector difference, so that the members of the same group with similar character- istics enhance each other's identities as group members. The proposed method is evaluated through gait-based person identification experiments using both simulated and real input sequences. Experimental results show that the identi- fication performance is considerably improved when compared with that of the straightforward method based on the gait feature alone.

Robust Gait-Based Person Identification against Walking Speed Variations

Robust Gait-Based Person Identification against Walking Speed Variations

2A1-L01 歩行者の見えの変化に頑強な個人識別(ロボットビジョン)

This paper presents a novel method for gait-based person identification robust to changes in appearance. Gait is sensitive to appearance changes, such as variations of clothes and carrying conditions, so the correct classification rate is reduced in case target's appearance condition is different from that in the database. So we propose a new part-based person identification method, where the discrimination capability at each part is directly controlled based on gait features between gallery datasets and probe dataset. Experiments using a gait database CASIA show the effectiveness of the proposed method.

Rate-Invariant Recognition of Humans and Their Activities

Pattern recognition in video is a challenging task because of the multitude of spatio-temporal variations that occur in different videos capturing the exact same event. While traditional pattern-theoretic approaches account for the spatial changes that occur due to lighting and pose, very little has been done to address the effect of temporal rate changes in the executions of an event. In this paper, we provide a systematic model-based approach to learn the nature of such temporal variations (time warps) while simultaneously allowing for the spatial variations in the descriptors. We illustrate our approach for the problem of action recognition and provide experimental justification for the importance of accounting for rate variations in action recognition. The model is composed of a nominal activity trajectory and a function space capturing the probability distribution of activity-specific time warping transformations. We use the square-root parameterization of time warps to derive geodesics, distance measures, and probability distributions on the space of time warping functions. We then design a Bayesian algorithm which treats the execution rate function as a nuisance variable and integrates it out using Monte Carlo sampling, to generate estimates of class posteriors. This approach allows us to learn the space of time warps for each activity while simultaneously capturing other intra- and interclass variations. Next, we discuss a special case of this approach which assumes a uniform distribution on the space of time warping functions and show how computationally efficient inference algorithms may be derived for this special case. We discuss the relative advantages and disadvantages of both approaches and show their efficacy using experiments on gait-based person identification and activity recognition.