A Grassmannian Approach to Address View Change Problem in Gait Recognition.

Gait recognition is a distinctive biometric technique that can identify pedestrians by their walking patterns from considerable distances. A critical challenge in gait recognition lies in effectively acquiring discriminative spatial-temporal representations from silhouettes that exhibit invariance to disturbances. In this paper, we present a novel gait recognition network by aggregating features in the spatial-temporal and view domains, which consists of two-path spatial-temporal feature fusion module and view embedding module. Specifically, two-path spatial-temporal feature fusion module firstly utilizes multi-scale feature extraction (MSFE) to enrich the input features with multiple convolution kernels of various sizes. Then, frame-level spatial feature extraction (FLSFE) and multi-scale temporal feature extraction (MSTFE) are parallelly constructed to capture spatial and temporal gait features of different granularities and these features are fused together to obtain muti-scale spatial-temporal features. FLSFE is designed to extract both global and local gait features by employing a specially designed residual operation. Simultaneously, MSTFE is applied to adaptively interact multi-scale temporal features and produce suitable motion representations in temporal domain. Taking into account the view information, we introduce a view embedding module to reduce the impact of differing viewpoints. Through the extensive experimentation over CASIA-B and OU-MVLP datasets, the proposed method has achieved superior performance to the other state-of-the-art gait recognition approaches.

Gait recognition, a rapidly advancing vision technology for person identification from a distance, has made significant strides in indoor settings. However, evidence suggests that existing methods often yield unsatisfactory results when applied to newly released real-world gait datasets. Furthermore, conclusions drawn from indoor gait datasets may not easily generalize to outdoor ones. Therefore, the primary goal of this paper is to present a comprehensive benchmark study aimed at improving practicality rather than solely focusing on enhancing performance. To this end, we developed OpenGait, a flexible and efficient gait recognition platform. Using OpenGait, we conducted in-depth ablation experiments to revisit recent developments in gait recognition. Surprisingly, we detected some imperfect parts of some prior methods and thereby uncovered several critical yet previously neglected insights. These findings led us to develop three structurally simple yet empirically powerful and practically robust baseline models: DeepGaitV2, SkeletonGait, and SkeletonGait++, which represent the appearance-based, model-based, and multi-modal methodologies for gait pattern description, respectively. In addition to achieving state-of-the-art performance, our careful exploration provides new perspectives on the modeling experience of deep gait models and the representational capacity of typical gait modalities. In the end, we discuss the key trends and challenges in current gait recognition, aiming to inspire further advancements towards better practicality.

Gait recognition stands as a pivotal biometric technology in individual identification, yet its real-world implementation faces challenges stemming from intra-subject disparities. The task of extracting consistent features to distinguish among various subjects becomes onerous due to factors such as image noise and magnitude divergence, significantly impacting recognition accuracy. In addressing this hurdle, we introduce a groundbreaking approach known as the Spline Magnitude Disparity Cross-Correlated Deep Network, designed to optimize gait recognition efficiency. Our method, the Spline Magnitude Disparity Cross-Correlated Deep Network, operates through two key steps: B-Spline magnitude disparity deformation (BS-MDD) registration and cross-correlated long-short gait recognition modeling. The BS-MDD algorithm employs free-form deformation to approximate the magnitude divergence in gait input, enhancing viewpoint optimization and contributing to the development of the cross-correlated model. By focusing on preserving high-output recognition gates while eliminating forget gates, our approach achieves a heightened recognition rate. Evaluation on the widely utilized CASIA B dataset showcases the superiority of our proposed method over state-of-the-art alternatives in terms of the true positive rate, false-positive rate, recognition time, and overall recognition rate. Notably, our approach elevates the true positive rate by 5% and reduces the false-positive rate by 4%. These results underscore the high effectiveness of our method, demonstrating its capacity to substantially improve the accuracy of gait recognition in practical applications.”

Gait recognition is an emerging biometric technology that can be used to protect the privacy of wearable device owners. To improve the performance of the existing gait recognition method based on wearable devices and to reduce the memory size of the model and increase its robustness, a new identification method based on multimodal fusion of gait cycle data is proposed. In addition, to preserve the time-dependence and correlation of the data, we convert the time-series data into two-dimensional images using the Gramian angular field (GAF) algorithm. To address the problem of high model complexity in existing methods, we propose a lightweight double-channel depthwise separable convolutional neural network (DC-DSCNN) model for gait recognition for wearable devices. Specifically, the time series data of gait cycles and GAF images are first transferred to the upper and lower layers of the DC-DSCNN model. The gait features are then extracted with a three-layer depthwise separable convolutional neural network (DSCNN) module. Next, the extracted features are transferred to a softmax classifier to implement gait recognition. To evaluate the performance of the proposed method, the gait dataset of 24 subjects were collected. Experimental results show that the recognition accuracy of the DC-DSCNN algorithm is 99.58%, and the memory usage of the model is only 972 KB, which verifies that the proposed method can enable gait recognition for wearable devices with lower power consumption and higher real-time performance.

Gait recognition is one of the new biometric techniques. However, gait recognition is easily affected by external factors, resulting in a decrease in the accuracy. Therefore, this paper proposes a gait recognition method based on spatial and temporal multi grained information. This method uses hierarchical network to obtain different receptive fields, focusing on both shallow and deep features of gait. In addition, more spatial and temporal information can be saved to improve the accuracy of gait recognition. Finally, the proposed method is experimentally verified on the CASIA-B dataset. the experimental results show that the accuracy of the proposed method is greatly improved under normal walking, carrying bags and wearing coats.

Surveillance scenes and pedestrian retrieval and recognition in videos gradually play an increasingly important role. Due to the long distance between the camera and pedestrians, the commonly used biometric identification methods such as face recognition and iris recognition cannot be applied to actual scenes. middle. The gait is a biological feature that is not easy to hide, not easy to camouflage, and can be obtained from a long distance, so it can be effectively used in surveillance and pedestrian retrieval and recognition in videos. Can be used as a feature to distinguish individuals. This paper proposes a framework for gait recognition based on Autoencoder, which can be uniformly converted to an angle that is conducive to retrieval and recognition without knowing the angle between the pedestrian and the camera in advance. The accuracy of gait recognition in the case of angle. The framework takes the characteristic gait energy map of commonly used gait recognition as input, and extracts better features to express the pedestrian's gait. This article is tested on the gait database dataset B collected by the Institute of Dynamics of the Chinese Academy of Sciences, that is, the public database of CAS worker A GaitDatabase B. The experimental results show that the framework can effectively solve the three common challenges in gait recognition, which are multi-view, carrying items and clothing changes, and achieve good results.

Gait recognition is one of the most important biometric technologies and has been applied in many fields. Recent gait recognition frameworks represent each gait frame by descriptors extracted from either global appearances or local regions of humans. However, the representations based on global information often neglect the details of the gait frame, while local region based descriptors cannot capture the relations among neighboring regions, thus reducing their discriminativeness. In this paper, we propose a novel feature extraction and fusion framework to achieve discriminative feature representations for gait recognition. Towards this goal, we take advantage of both global visual information and local region details and develop a Global and Local Feature Extractor (GLFE). Specifically, our GLFE module is composed of our newly designed multiple global and local convolutional layers (GLConv) to ensemble global and local features in a principle manner. Furthermore, we present a novel operation, namely Local Temporal Aggregation (LTA), to further preserve the spatial information by reducing the temporal resolution to obtain higher spatial resolution. With the help of our GLFE and LTA, our method significantly improves the discriminativeness of our visual features, thus improving the gait recognition performance. Extensive experiments demonstrate that our proposed method outperforms state-of-the-art gait recognition methods on two popular datasets.

We propose a new gait feature extraction method for gait recognition that uses the dyadic wavelet transform (DYWT) and structural similarity (SSIM). Gait recognition is a person authentication technique that uses the gait features obtained from a monitoring camera video. Most existing gait recognition methods use gait features based on the silhouette image sequence. However, the gait recognition accuracy decreases if the resolution of the silhouette image is low. We developed a new method to extract gait features from silhouette images having a low-resolution gait period sequence that uses the DYWT and SSIM. For our experiment, we prepared two types of silhouette image sequences of 100 subjects for use as the probe and gallery images, respectively. The sizes of the silhouette images were 64 × 44, 32 × 22, and 16 × 11. We describe our proposed method in detail and present our experimental results demonstrating that the extracted gait features are effective.

Gait recognition systems often suffer from the challenges when query gaits are under the coupled effects of unknown view angles and large intra-class variations (e.g., wearing a coat). In this paper, we deem it as a two-stage classification problem, namely, view detection and fixed-view gait recognition. First, we propose two simple yet effective feature types (i.e., global features and local features) for view detection. By using the detected view information, the corresponding gallery (i.e., enrolled gait) for the detected view can be adaptively selected to perform the fixed-view gait recognition. For fixed-view gait recognition, since the inter-class variations for training are normally small, whereas the query gait usually has large intra-class variations, random subspace method are adopted. We evaluate our approach on the largest multi-view gait database CASIA-B dataset. The avoidance of searching whole multi-view database as well as the competitive performance indicate that our proposed method is practical for gait recognition in real world surveillance scenarios.

It is well recognized that gait is an important biometric feature to identify a person at a distance, e.g., in video surveillance application. However, in reality, change of viewing angle causes significant challenge for gait recognition. A novel approach using regression-based view transformation model (VTM) is proposed to address this challenge. Gait features from across views can be normalized into a common view using learned VTM(s). In principle, a VTM is used to transform gait feature from one viewing angle (source) into another viewing angle (target). It consists of multiple regression processes to explore correlated walking motions, which are encoded in gait features, between source and target views. In the learning processes, sparse regression based on the elastic net is adopted as the regression function, which is free from the problem of overfitting and results in more stable regression models for VTM construction. Based on widely adopted gait database, experimental results show that the proposed method significantly improves upon existing VTM-based methods and outperforms most other baseline methods reported in the literature. Several practical scenarios of applying the proposed method for gait recognition under various views are also discussed in this paper.

Gait recognition has become a mainstream technology for identification, as it can recognize the identity of subjects from a distance without any cooperation. However, when subjects wear coats (CL) or backpacks (BG), their gait silhouette will be occluded, which will lose some gait information and bring great difficulties to the identification. Another important challenge in gait recognition is that the gait silhouette of the same subject captured by different camera angles varies greatly, which will cause the same subject to be misidentified as different individuals under different camera angles. In this article, we try to overcome these problems from three aspects: data augmentation, feature extraction, and feature refinement. Correspondingly, we propose gait sequence mixing (GSM), multigranularity feature extraction (MFE), and feature distance alignment (FDA). GSM is a method that belongs to data enhancement, which uses the gait sequences in NM to assist in learning the gait sequences in BG or CL, thus reducing the influence of lost gait information in abnormal gait sequences (BG or CL). MFE explores and fuses different granularity features of gait sequences from different scales, and it can learn as much useful information as possible from incomplete gait silhouettes. FDA refines the extracted gait features with the help of the distribution of gait features in real world and makes them more discriminative, thus reducing the influence of various camera angles. Extensive experiments demonstrate that our method has better results than some state-of-the-art methods on CASIA-B and mini-OUMVLP. We also embed the GSM module and FDA module into some state-of-the-art methods, and the recognition accuracy of these methods is greatly improved.

In recent decades, gait recognition has garnered a lot of attention from the researchers in the IT era. Gait recognition signifies verifying or identifying the individuals by their walking style. Gait supports in surveillance system by identifying people when they are at a distance from the camera and can be used in numerous computer vision and surveillance applications. This paper proposes a stupendous Color-mapped Contour Gait Image (CCGI) for varying factors of Cross-View Gait Recognition (CVGR). The first contour in each gait image sequence is extracted using a Combination of Receptive Fields (CORF) contour tracing algorithm which extracts the contour image using Difference of Gaussians (DoG) and hysteresis thresholding. Moreover, hysteresis thresholding detects the weak edges from the total pixel information and provides more well-balanced smooth features compared to an absolute one. Second CCGI encodes the spatial and temporal information via color mapping to attain the regularized contour images with fewer outliers. Based on the front view of a human walking pattern, the appearance of cross-view variations would reduce drastically with respect to a change of view angles. This proposed work evaluates the performance analysis of CVGR using Deep Convolutional Neural Network (CNN) framework. CCGI is considered a gait feature for comparing and evaluating the robustness of our proposed model. Experiments conducted on CASIA-B database show the comparisons of previous methods with the proposed method and achieved 94.65% accuracy with a better recognition rate.

Gait recognition is a unique biometric technique that can be performed at a long distance non-cooperatively and has broad applications in public safety and intelligent traffic systems. Previous gait works focus more on minimizing the intra-class variance while ignoring the significance in constraining inter-class variance. To this end, we propose a generalized inter-class loss which resolves the inter-class variance from both sample-level feature distribution and class-level feature distribution. Instead of equal penalty strength on pair scores, the proposed loss optimizes sample-level inter-class feature distribution by dynamically adjusting the pairwise weight. Further, in class-level distribution, generalized inter-class loss adds a constraint on the uniformity of inter-class feature distribution, which forces the feature representations to approximate a hypersphere and keep maximal inter-class variance. In addition, the proposed method automatically adjusts the margin between classes which enables the inter-class feature distribution to be more flexible. The proposed method can be generalized to different gait recognition networks and achieves significant improvements. We conduct a series of experiments on CASIA-B and OUMVLP, and the experimental results show that the proposed loss can significantly improve the performance and achieves the state-of-the-art performances.

Human motion analysis is a dynamic field of research in computer vision. Its popularity is because of wide application in surveillance, study of social interaction, robot guidance and video indexing. Human motion can be considered as dynamic texture since it has statistical variation in spatiotemporal domain. The local interest features contain efficient information of these spatiotemporal variations. The proposed method is based on dynamic texture description for analysis of human motion using visual dictionary. This method is applied to two major applications: action recognition and gait recognition. We evaluate the performance of our method on KTH dataset for both action and gait recognition.

With the rapid development of wearable Internet of Things (WIoT) devices, a significant amount of sensitive/private information collected by them poses a considerable challenge to the security of the WIoT devices. The accelerometer-based gait recognition is considered as an emerging and fast-evolving technology in security and access control fields and has achieved outstanding performance at certain fixed walking speeds. However, the gait recognition performance of the above technology deteriorates dramatically when the walking speed varies. To address this issue, both the speed-adaptive gait cycle segmentation method and individualized matching threshold generation method were proposed in this paper. Furthermore, the contrast experiments were conducted on the ZJU-GaitAcc public dataset sampled from five different body locations and the self-collected dataset sampled at various walking speeds. The experimental results indicated the average gait recognition and user authentication rates of 96.9% and 91.75%, respectively. As compared to the available state-of-the-art methods based on the fixed walking speeds and constant thresholds, the proposed method improved the gait recognition by 25.8% and user authentication by 21.5%.