Gait Recognition and Understanding Based on Hierarchical Temporal Memory Using 3D Gait Semantic Folding.

The paper explores the challenge of human identification through gait recognition within biometric identification systems. Itoutlines the essential criteria for human biometric features, discusses primary biometric characteristics, and their application in biometric identification systems. The paper also examines the feasibility of utilizing gait as a biometric identifier, emphasizing its advantages, such as not requiring the upfront provision of personal biometric information and specialized equipment.The authors conduct an analysis of existing scientific literature in the field of gait recognition, categorizing gait recognition methodsinto template-based and non-template-based approaches. Throughout their research, they identify the key issues and challenges that researchers face in this domain, along with the prevailing trends in human gait recognition within biometric identification systems.Additionally, the paper introduces a method for person identification based on gait, utilizing the Histogram of Oriented Gradients and the Sum Variance Haralick texture features. It involves transforming input video into a series of images depicting the gait silhouette, creating a Gait Energy Image (GEI) by combining these gait silhouettes throughout a gait cycle, and translating the GEI into the Gait Gradient Magnitude Image (GGMI). The subsequent step involves extracting recommended gait characteristics from the GGMIs of participants included in a dataset.To preprocess the collected characteristics, Principal Component Analysis (PCA) is applied, reducing the dimensions that may negatively impact classification robustness, thereby enhancing overall performance. In the final step, a K-Nearest Neighbors (KNN) classifier is employed to categorize the characteristics obtained from a specific dataset.The proposed novel feature vector in the paper demonstrates increased reliability and effectively captures spatial variations in gait patterns. Notably, it reduces the dimensionality of the feature vector from 3780×1 to 63×1, resulting in decreased computational complexity in the gait recognition system. Experimental evaluations on the CASIA A and CASIA B datasets reveal that the proposed approach outperforms other HOG-based methods in most scenarios, with the exception of situations involving frontal images.

Machine learning type neuromorphic algorithms have the potential to enable the brains behind small autonomous robots, provided these algorithms can be implemented energy efficiently. The implementation difficulties are mainly extremely large memory size and high memory bandwidth making Von Neumann computational model realizations inefficient. However, these algorithms are inherently error robust, which may be taken advantage of in the realizations. For example, in with low operating voltage stochastic hardware. To determine the hardware requirements for an Application Specific Integrated Circuit (ASIC) realization, an example algorithm, Hierarchical Temporal Memory (HTM), is realized here. A 64×64 HTM network with 1440 connections per elementary processing element requires 530 mm2 area, 340 Mb memory, and a 10MHz clock frequency in 65nm technology. With point-to-point connections these processing elements would have a communication radius of circa 50 elements and on-chip wideband technologies can increase the range further.

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.

In real-world scenarios, walking/running speed is one of the most common covariate factors that can affect the performance of gait recognition systems. By assuming the effect caused by the speed changes (from the query walker-s/runners) are intra-class variations that the training data (i.e., gallery) fails to capture, overfitting to the less representative training data may be the main problem that degrades the performance. In this work, we employ a general model based on random subspace method to solve this problem. More specifically, for query gaits in unknown speeds, we try to reduce the generalization errors by combining a large number of weak classifiers. We evaluate our method on two benchmark databases, i.e., Infrared CASIA-C dataset and Treadmill OU-ISIR-A dataset. For the cross-speed walking/running gait recognition experiments, nearly perfect results are achieved, significantly higher than other state-of-the-art algorithms. We also study the unknown- speed nrunner identification solely using the walking gait gallery, and the encouraging experimental results suggest the effectiveness of our method in such cross-mode gait recognition tasks.

The selective attention mechanism is employed by humans and primates to realize a truly intelligent perception system, which has the cognitive capability of learning and thinking about how to perceive the environment autonomously. The attention mechanism involves the top-down and bottom-up ways that correspond to the goal-directed and automatic perceptual behaviors, respectively. Rather than considering the automatic perception, this paper presents an artificial system of the goal-directed visual perception by using the object-based top-down visual attention mechanism. This cognitive system can guide the perception to an object of interest according to the current task, context and learned knowledge. It consists of three successive stages: preattentive processing, top-down attentional selection and post-attentive perception. The preattentive processing stage divides the input scene into homogeneous proto-objects, one of which is then selected by the top-down attention and finally sent to the post-attentive perception stage for high-level analysis. Experimental results of target detection in the cluttered environments are shown to validate this system.

This paper proposes certain features for human gait cycle detection and recognition. The features cover both the categories of holistic and model-based approaches for human gait recognition. A unique feature vector is formed from the spatial-temporal silhouettes and Support Vector Machine (SVM) classifier is used for the identification of individuals through their gait. The present work is concerned with the efficiency of the extracted features. Experimentation on the silhouette samples of publicly available CASIA database has given furnishes promising results.

Gait recognition has become popular due to the rising demand for nonintrusive biometrics. At its nascent stage of development, gait recognition faces a number of challenges. The performance of a gait recognition system is sensitive towards factors like viewing angle, clothing, shoe type, load carriage and speed changes. In this paper, the problems of gait are formulated on the Grassmann manifold. It is not difficult to obtain multiple snapshots of a walking subjects with the wide availability of camera networks. These sets of images can be modelled as low-dimensional subspaces, which can be realized naturally as points on the Grassmann manifold. Modelling image sets as low-dimensional subspaces provides not only possible clue of one’s gait, but also the common patterns of variation in the set. We present a method called Localized Grassmann Mean Representatives with Partial Least Squares Regression (LoGPLS) to infer a low-dimensional Euclidean approximation of the manifold. The notion of local mean representatives is introduced to construct multiple tangent spaces to better approximate the topological structure of the manifold. As the properties of the tangent spaces allows the Grassmann points to be evaluated in the vector space, partial least squares is applied to allow a more accurate classification of the points in a reduced space. Experiments have been conducted on four different publicly available gait databases. Empirical evidences demonstrate the effectiveness of the proposed approach in solving the various covariates in gait recognition.

Gait recognition is an evolving technology in the biometric domain; it aims to recognize people through an analysis of their walking pattern. One of the significant challenges of the appearance-based gait recognition system is to augment its performance by using a distinctive low-dimensional feature vector. Therefore, this study proposes the low-dimensional features that are capable of effectively capturing the spatial, gradient, and texture information in this context. These features are obtained by the computation of histogram of oriented gradients, followed by sum variance Haralick texture descriptor from nine cells of gait gradient magnitude image. Further, the performance of the proposed method is validated on five widely used gait databases. They include CASIA A gait database, CASIA B gait database, OU-ISIR D gait database, CMU MoBo database, and KTH video database. The experimental results demonstrated that the proposed approach could choose significant discriminatory features for individual identification and consequently, outperform certain state-of-the-art methods in terms of recognition performance.

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.

View-independent human motion classification using image-based reconstruction

Gait is one of well‐identified biometrics that has been broadly applied for human identification at a distance based on their motion style. However, the current gait recognition might have difficulties due to changing the viewing angles anduncertainty associated with gait signature extraction. This study deals with the design of an intelligent gait recognition system that tackles the problems mentioned above. This system is based on spatial‐domain energy deviation image as a gait signature by adopting clustering technique to estimate the gait period in the gait sequence with arbitrary walking directions. To further improve the performance of the proposed system, interval type‐2 fuzzy K ‐nearest neighbor classifier is used to diminish the effect of uncertainty formed by variations in gait signature extraction. Interval type‐2 fuzzy set is involved in extending themembership values of each gait signature by using several initial K in order to handle and manage uncertainty that exists in choosing the initial value K . The proposed method realises the reduction in the dimensions of the gait feature and over‐fitting. The comprehensive analyses reveal that the proposed algorithm can significantly enhance the multiple view gait recognition performance when being matched to the similar methods in the literature.

Gait recognition, crucial in biometrics and behavioral analytics, has applications in human–computer interaction, identity verification, and health monitoring. Traditional sensors face limitations in complex or poorly lit settings. RF-based approaches, particularly millimeter-wave technology, are gaining traction for their privacy, insensitivity to light conditions, and high resolution in wireless sensing applications. In this paper, we propose a gait recognition system called Multidimensional Point Cloud Gait Recognition (PGGait). The system uses commercial millimeter-wave radar to extract high-quality point clouds through a specially designed preprocessing pipeline. This is followed by spatial clustering algorithms to separate users and perform target tracking. Simultaneously, we enhance the original point cloud data by increasing velocity and signal-to-noise ratio, forming the input of multidimensional point clouds. Finally, the system inputs the point cloud data into a neural network to extract spatial and temporal features for user identification. We implemented the PGGait system using a commercially available 77 GHz millimeter-wave radar and conducted comprehensive testing to validate its performance. Experimental results demonstrate that PGGait achieves up to 96.75% accuracy in recognizing single-user radial paths and exceeds 94.30% recognition accuracy in the two-person case. This research provides an efficient and feasible solution for user gait recognition with various applications.

Gait recognition based on gait silhouette profiles is currently a major approach in the field of gait recognition. In previous studies, models typically used gait silhouette images sized at 64 × 64 pixels as input data. However, in practical applications, cases may arise where silhouette images are smaller than 64 × 64, leading to a loss in detail information and significantly affecting model accuracy. To address these challenges, we propose a gait recognition system named Multi-scale Feature Cross-Fusion Gait (MFCF-Gait). At the input stage of the model, we employ super-resolution algorithms to preprocess the data. During this process, we observed that different super-resolution algorithms applied to larger silhouette images also affect training outcomes. Improved super-resolution algorithms contribute to enhancing model performance. In terms of model architecture, we introduce a multi-scale feature cross-fusion network model. By integrating low-level feature information from higher-resolution images with high-level feature information from lower-resolution images, the model emphasizes smaller-scale details, thereby improving recognition accuracy for smaller silhouette images. The experimental results on the CASIA-B dataset demonstrate significant improvements. On 64 × 64 silhouette images, the accuracies for NM, BG, and CL states reached 96.49%, 91.42%, and 78.24%, respectively. On 32 × 32 silhouette images, the accuracies were 94.23%, 87.68%, and 71.57%, respectively, showing notable enhancements.

Today, many different biometric features are used for human identification. Unlike biometric features, such as eye, iris, ear, and fingerprint, gait biometrics enables recognition from long distance and low resolution images. In this paper, different design choices for a deep learning-based gait recognition system are investigated in detail. Some preprocessing steps, such as human silhouette extraction and gait cycle calculation are eliminated to make the system suitable for practical applications. To assess different input types’ effect on the gait recognition performance, both binary silhouettes and RGB images are given as input to the network. To observe the contribution of transfer learning, we fine-tuned a pre-trained generic object recognition model with the CASIA-B gait dataset and performed experiments on the OU-ISIR Large Population gait dataset. To observe the effect of pose variations, we conducted experiments for both identical-view and cross-view conditions. Successful results are obtained, especially for cross-view gait recognition, compared to different approaches for gait recognition.

Gait recognition appears to be a valuable asset when conventional biometrics cannot be employed. Nonetheless, recognizing human by gait is not a trivial task due to the complex human kinematic structure and other external factors affecting human locomotion. A major challenge in gait recognition is view variation. A large difference between the views in the query and reference sets often leads to performance deterioration. In this paper, we show how to generate virtual views to compensate the view difference in the query and reference sets, making it possible to match the query and reference sets using standardized views. The proposed method, which combines multiview matrix representation and a novel randomized kernel extreme learning machine, is an end-to-end solution for view change problem under Grassmann manifold treatment. Under the right condition, the view-tagging problem can be eliminated. Since the recording angle and walking direction of the subject are not always available, this is particularly valuable for a practical gait recognition system. We present several working scenarios for multiview recognition that have not be considered before. Rigorous experiments have been conducted on two challenging benchmark databases containing multiview gait datasets. Experiments show that the proposed approach outperforms several state-of-the-arts methods.