Improved Gait Recognition Research Articles

Multimodal biometric fusion sentiment analysis of teachers and students based on classroom surveillance video streaming

Abstract In the education system, teachers and students as the main body of the classroom; their emotional state in the classroom school is an important indicator of the effectiveness of the classroom. This study first explores biometric recognition, based on the needs of the classroom curriculum and the classroom monitoring as a sensor, to propose a multimodal biometric fusion detection method based on the fusion of face and gait recognition. The PCA algorithm is used to optimize the face recognition as well as the occlusion situation in the classroom to improve gait recognition, and then the face and gait are fused based on the decision layer to achieve the detection and recognition of the identity situation of teachers and students. On this basis, an expression recognition model is established using the attention mechanism, and an emotion analysis system is designed for the classroom curriculum. According to the empirical evidence of multimodal biometric fusion sentiment analysis, the mAP accuracy of this paper’s fusion method is 100% in Euclidean distance, and the accuracy is higher than 99% in cosine distance, which is obviously better than other methods, and the accuracy of this paper’s fusion recognition is above 95% under any condition limitations. At the same time, the correct rate of recognition of emotions such as listening, appreciation, resistance, doubt, and inattention are all higher than 85%, and the five indexes of average absolute error, Pearson correlation coefficient, Accuarcy5, Accuarcy2, and F12 score of this paper’s sentiment analysis have achieved the best results comparing with other sentiment analysis models, which proves the generalization and validity of this paper’s sentiment analysis.

Using Skeleton Correction to Improve Flash Lidar-based Gait Recognition

ABSTRACT This paper presents GlidarPoly, an efficacious pipeline of 3D gait recognition for flash lidar data based on pose estimation and robust correction of erroneous and missing joint measurements. A flash lidar can provide new opportunities for gait recognition through a fast acquisition of depth and intensity data over an extended range of distance. However, the flash lidar data are plagued by artifacts, outliers, noise, and sometimes missing measurements, which negatively affects the performance of existing analytics solutions. We present a filtering mechanism that corrects noisy and missing skeleton joint measurements to improve gait recognition. Furthermore, robust statistics are integrated with conventional feature moments to encode the dynamics of the motion. As a comparison, length-based and vector-based features extracted from the noisy skeletons are investigated for outlier removal. Experimental results illustrate the superiority of the proposed methodology in improving gait recognition given noisy, low-resolution flash lidar data.

The Impact of Load Style Variation on Gait Recognition Based on sEMG Images Using a Convolutional Neural Network.

Surface electromyogram (sEMG) signals are widely employed as a neural control source for lower-limb exoskeletons, in which gait recognition based on sEMG is particularly important. Many scholars have taken measures to improve the accuracy of gait recognition, but several real-time limitations affect its applicability, of which variation in the load styles is obvious. The purposes of this study are to (1) investigate the impact of different load styles on gait recognition; (2) study whether good gait recognition performance can be obtained when a convolutional neural network (CNN) is used to deal with the sEMG image from sparse multichannel sEMG (SMC-sEMG); and (3) explore whether the control system of the lower-limb exoskeleton trained by sEMG from part of the load styles still works efficiently in a real-time environment where multiload styles are required. In addition, we discuss an effective method to improve gait recognition at the levels of the load styles. In our experiment, fifteen able-bodied male graduate students with load (20% of body weight) and using three load styles (SBP = backpack, SCS = cross shoulder, SSS = straight shoulder) were asked to walk uniformly on a treadmill. Each subject performed 50 continuous gait cycles under three speeds (V3 = 3 km/h, V5 = 5 km/h, and V7 = 7 km/h). A CNN was employed to deal with sEMG images from sEMG signals for gait recognition, and back propagation neural networks (BPNNs) and support vector machines (SVMs) were used for comparison by dealing with the same sEMG signal. The results indicated that (1) different load styles had remarkable impact on the gait recognition at three speeds under three load styles (p < 0.001); (2) the performance of gait recognition from the CNN was better than that from the SVM and BPNN at each speed (84.83%, 81.63%, and 83.76% at V3; 93.40%, 88.48%, and 92.36% at V5; and 90.1%, 86.32%, and 85.42% at V7, respectively); and (3) when all the data from three load styles were pooled as testing sets at each speed, more load styles were included in the training set, better performance was obtained, and the statistical analysis suggested that the kinds of load styles included in training set had a significant effect on gait recognition (p = 0.002), from which it can be concluded that the control system of a lower-limb exoskeleton trained by sEMG using only some load styles is not sufficient in a real-time environment.

Human Gait Recognition Based on Multiple Feature Combination and Parameter Optimization Algorithms.

Accuracy is a key index of human gait recognition. In this paper, we propose an improved gait recognition algorithm, which combines multiple feature combination and artificial bee colony for optimizing the support vector machine (ABC-SVM). Firstly, considering the complexity characteristics of surface electromyography (sEMG) signals, four types of features are extracted from the denoised sEMG signals, including the time-domain features of integral of absolute value (IAV), variance (VAR), and number of zero-crossing (ZC) points, frequency-domain features of mean power frequency (MPF) and median frequency (MF), and wavelet features and fuzzy entropy features. Secondly, the classifiers of SVM, linear discriminant analysis (LDA), and extreme learning machine (ELM) are employed to recognize the gait with obtained features, including singe-class features, multiple combination features, and optimized features of dimension reduction by principal component analysis (PCA). Thirdly, the penalty coefficient and kernel function parameter of the SVM classifier are optimized by the ABC algorithm, and the influence of different features and classifiers on the recognition results is studied. Finally, the feature samples selected to construct the SVM classifier are trained and recognized. Results show that the classification performance of the ABC-SVM classifier is significantly better than that of the nonoptimized SVM classifier, and the average recognition rate is increased by 3.18%. In addition, the combined feature samples (time-domain, frequency-domain, wavelet, and fuzzy entropy features) not only improve the gait classification accuracy but also enhance the recognition stability.

Improved gait recognition through gait energy image partitioning

AbstractRecently, human gait pattern has turned into an essential biometric feature to recognize an individual remotely. Gait as a feature becomes challenging owing to variation in appearance under different covariate conditions (eg, shoe, surface, haul, viewpoint and attire). The covariates may alter few fragment of gait while other fragment stay unaltered, leading to lower the probability of correct identification. To overcome such variation, an improved gait recognition strategy is proposed in this article by gait energy image partitioning and selection processing. Our method involves pre‐processing of raw video for silhouette extraction, gait cycle detection, segmentation into different regions, and histogram of gradients feature extraction from selected segments. In this way, the specific features across complete gait cycles are extracted precisely. Finally, recognition is done by using K‐NN. The proposed strategy has been assessed using the CASIA B gait database. Our outcomes shows a particular proposed strategy accomplishes high recognition rate and outperforms the advanced gait recognition mechanism.

Joint Metric Learning and Hierarchical Network for Gait Recognition

Gait recognition aims to identify person by walking pattern of individuals, which has a valuable application prospect in the fields of identity identification, public security and medical diagnosis. At present, most of the gait recognition methods are designed based on the human gait information. However, this paper focuses on the feature space of gait recognition feature learning, and optimizes the feature space by introducing some general criteria to improve the discrimination of depth features. In recent literature, optimizing the feature space has been proven to be beneficial for personal identification. Taken above insights together, we propose a novel gait recognition method based on joint metric learning and hierarchical network in an end-to-end manner, for improving the ability of feature identification. The contribution of this paper is two-fold. First, a joint metric learning and hierarchical network is used to realize the learning of fine-grained information and accelerated the discriminative feature learning. Second, to learn discriminative feature, we use a fuse loss function, the triplet loss and classifier loss. It guides the training process, pulls the samples in the same set close to each other, pushes the samples in different sets away from each other, and takes the silhouettes in each set as a whole. We evaluate the proposed method on two widely-used gait datasets, ie. CASIA-B and OU-LP-Bag. Experimental results demonstrate the effectiveness of the proposed method, significantly improve gait recognition performances under cross-view and bag-carrying walking conditions, respectively. Particularly, we achieves Rank-1 accuracy of 93.2% on OU-LP-Bag dataset, better than existing state-of-the art.

Improved gait recognition based on specialized deep convolutional neural network

Gait recognition is a biometric technique used in determining the identity of humans based on the style and the manner of their walk. Its performance is often degraded by covariate factors such as carrying condition changes, clothing condition changes, and viewing angle variations. Recently, machine learning based techniques have produced promising results for challenging classification problems. Since, a deep convolutional neural network (CNN) is one of the most advanced machine learning techniques with the ability to approximate complex non-linear functions, we develop a specialized deep CNN architecture for Gait Recognition. The proposed architecture is less sensitive to several cases of the common variations and occlusions that affect and degrade gait recognition performance. It can also handle relatively small data sets without using any augmentation or fine-tuning techniques. The majority of previous approaches to gait recognition have used subspace learning methods which have several shortcomings that we avoid. Our specialized deep CNN model can obtain competitive performance when tested on the CASIA-B large gait dataset.

Individuality-preserving Silhouette Extraction for Gait Recognition

Most gait recognition approaches rely on silhouette-based representations due to high recognition accu- racy and computational efficiency, and a key problem for those approaches is how to accurately extract individuality- preserved silhouettes from real scenes, where foreground colors may be similar to background colors and the back- groundis cluttered. We thereforeproposea method of individuality-preservingsilhouetteextractionfor gait recognition using standard gait models (SGMs) composed of clean silhouette sequences of a variety of training subjects as a shape prior. We firstly match the multiple SGMs to a background subtraction sequence of a test subject by dynamic pro- gramming and select the training subject whose SGM fit the test sequence the best. We then formulate our silhouette extraction problem in a well-established graph-cut segmentation framework while considering a balance between the observed test sequence and the matched SGM. More specifically, we define an energy function to be minimized by the following three terms: (1) a data term derived from the observed test sequence, (2) a smoothness term derived from spatio-temporally adjacent edges, and (3) a shape-prior term derived from the matched SGM. We demonstrate that the proposed method successfully extracts individuality-preserved silhouettes and improved gait recognition accuracy through experiments using 56 subjects.

Improved gait recognition by multiple-projections normalization

Recognizing people by gait promises to be useful for identifying individuals from a distance; in this regard, improved techniques are under development. In this paper, an improved method for gait recognition is proposed. Binarized silhouette of a motion object is first represented by four 1-D signals that are the basic image features called the distance vectors. The distance vectors are differences between the bounding box and silhouette, and extracted using four projections to silhouette. Fourier Transform is employed as a preprocessing step to achieve translation invariant for the gait patterns accumulated from silhouette sequences that are extracted from the subjects’ walk in different speed and/or different time. Then, eigenspace transformation is applied to reduce the dimensionality of the input feature space. Support vector machine (SVM)-based pattern classification technique is then performed in the lower-dimensional eigenspace for recognition. The input feature space is alternatively constructed by using two different approaches. The four projections (1-D signals) are independently classified in the first approach. A fusion task is then applied to produce the final decision. In the second approach, the four projections are concatenated to have one vector and then pattern classification with one vector is performed in the lower-dimensional eigenspace for recognition. The experiments are carried out on the most well-known public gait databases: the CMU, the USF, SOTON, and NLPR human gait databases. To effectively understand the performance of the algorithm, the experiments are executed and presented as increasing amounts of the gait cycles of each person available during the training procedure. Finally, the performance of the proposed algorithm is comparatively illustrated to take into consideration the published gait recognition approaches.

Improved gait recognition by gait dynamics normalization

Potential sources for gait biometrics can be seen to derive from two aspects: gait shape and gait dynamics. We show that improved gait recognition can be achieved after normalization of dynamics and focusing on the shape information. We normalize for gait dynamics using a generic walking model, as captured by a population Hidden Markov Model (pHMM) defined for a set of individuals. The states of this pHMM represent gait stances over one gait cycle and the observations are the silhouettes of the corresponding gait stances. For each sequence, we first use Viterbi decoding of the gait dynamics to arrive at one dynamics-normalized, averaged, gait cycle of fixed length. The distance between two sequences is the distance between the two corresponding dynamics-normalized gait cycles, which we quantify by the sum of the distances between the corresponding gait stances. Distances between two silhouettes from the same generic gait stance are computed in the linear discriminant analysis space so as to maximize the discrimination between persons, while minimizing the variations of the same subject under different conditions. The distance computation is constructed so that it is invariant to dilations and erosions of the silhouettes. This helps us handle variations in silhouette shape that can occur with changing imaging conditions. We present results on three different, publicly available, data sets. First, we consider the HumanlD Gait Challenge data set, which is the largest gait benchmarking data set that is available (122 subjects), exercising five different factors, i.e., viewpoint, shoe, surface, carrying condition, and time. We significantly improve the performance across the hard experiments involving surface change and briefcase carrying conditions. Second, we also show improved performance on the UMD gait data set that exercises time variations for 55 subjects. Third, on the CMU Mobo data set, we show results for matching across different walking speeds. It is worth noting that there was no separate training for the UMD and CMU data sets.

Classification of multivariate gait EMG patterns in cerebral palsy

automatic recognition of human movement is an effective strategy to assess abnormal gait patterns. Machine learning approaches are mainly applied due to their ability to work with multidimensional nonlinear features.to compare several machine learning algorithms employed for gait pattern recognition in motor disorders using discriminant features extracted from gait dynamics. Additionally, this work highlights procedures that improve gait recognition performance.we conducted an electronic literature search on Web of Science, IEEE, and Scopus, using “human recognition”, “gait patterns’’, and “feature selection methods” as relevant keywords.analysis of the literature showed that kernel principal component analysis and genetic algorithms are efficient at reducing dimensional features due to their ability to process nonlinear data and converge to global optimum. Comparative analysis of machine learning performance showed that support vector machines (SVMs) exhibited higher accuracy and proper generalization for new instances.automatic recognition by combining dimensional data reduction, cross-validation and normalization techniques with SVMs may offer an objective and rapid tool for investigating the subject's clinical status. Future directions comprise the real-time application of these tools to drive powered assistive devices in free-living conditions.