A view-invariant gait recognition algorithm based on a joint-direct linear discriminant analysis

Face recognition using direct, weighted linear discriminant analysis and modular subspaces

Face recognition using direct, weighted linear discriminant analysis and modular subspaces

Gait energy image (GEI) is composed of static body silhouette and dynamic frequency information of human gait. To achieve fast and efficient gait recognition, combined with the accurate description of the information of details and directions in image by Curvelet transform, a gait recognition method using GEI and Curvelet (GEIC) is presented. Firstly, to gain the gait energy images, the gait cycle is selected according to the aspect ratio. Secondly, Curvelet energy coefficients of the GEI, which are used as gait feature vector, are extracted by Curvelet transform in different scales and different directions. Finally, the gait recognition is accomplished by the K nearest neighbor (KNN) classifier. The experimental results demonstrate that GEIC performs well on CASIA(B) database, with the average accuracy of 86.83%. Compared with GEI+KPCA, GEI+W(2D)2PCA and GEI+(2D)2PCA, the algorithm GEIC achieves better robustness in the condition of the person wearing or packaging.

Regularized discriminant analysis for the small sample size problem in face recognition

An effective gait recognition approach based on the gait energy image (GEI) representation is proposed. Synthetic GEIs are first created to address the problem of lacking training data. Then the Gabor phase spectrum of the GEI which was ignored in previous works is utilised as an input feature, and it is subsequently projected into a low dimensional space by linear discriminant analysis to perform classification. Experimental results show that the proposed approach outperforms other algorithms in terms of recognition accuracy.

Gait energy image (GEI) is considered as an effective gait representation for gait-based human identification. In gait recognition, normally, GEI is computed 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 a rise to degrading gait identification rate. In this paper, we address this issue by proposing a novel method to reconstruct a complete GEI from a few frames of gait cycle. 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 fully convolutional networks independently and then combining these as a uniform model. Experimental results on a large public gait dataset, namely OULP demonstrate the validity of the proposed method for gait identification when dealing with very incomplete gait cycles.

Gait Energy Image (GEI) is an efficient template for human identification by gait. However, such a template loses temporal information in a gait sequence, which is critical to the performance of gait recognition. To address this issue, we develop a novel temporal template, named Chrono-Gait Image (CGI), in this paper. The proposed CGI template first extracts the contour in each gait frame, followed by encoding each of the gait contour images in the same gait sequence with a multichannel mapping function and compositing them to a single CGI. To make the templates robust to a complex surrounding environment, we also propose CGI-based real and synthetic temporal information preserving templates by using different gait periods and contour distortion techniques. Extensive experiments on three benchmark gait databases indicate that, compared with the recently published gait recognition approaches, our CGI-based temporal information preserving approach achieves competitive performance in gait recognition with robustness and efficiency.

One-dimensional vs. two-dimensional based features: Plant identification approach

Gait recognition with cross-domain transfer networks

Gait recognition has become a popular research problem gaining importance for human identification based on walking style. It has emerged as an attractive research problem due to possessing several desirable merits unlike other biometrics. However, most of the existing gait recognition methods that involve Gabor-based filters suffer from the curse of dimensionality, even with the use of a dimensionality reduction technique. This adds more computational and storage burdens and may cause difficulties to identify subjects with a high degree of confidence. In this paper a statistical gait recognition approach is proposed based on the analysis of overlapping Gabor-based regions. The Gait Energy Image (GEI) is first constructed from the gait sequence as a spatio-temporal summary. Then, the GEI image is convolved with a Gabor filter bank of 8 different orientations and 5 different scales. A statistical analysis is then applied to extract discriminative gait features from multi-overlapped Gabor-based regions. Consecutively, an SVM classifier is applied to measure the gait similarity and identify the subject. Comprehensive experiments are carried out to evaluate the proposed approach and compare it to existing approaches. The results have shown that promising performance can be achieved with the proposed approach under a variety of scenarios.

In order to improve the recognition performance and solve the problem of computational complexity caused by the high-dimensional data in human identification, a gait recognition method based on manifold learning is proposed in this paper. Firstly, gait energy image (GEI) of a walking person is abstracted from a gait image sequence. And then discrete wavelet decomposition (DWT) and t-Distributed Stochastic Neighbor Embedding (t-SNE) method is applied to reduce the dimension of high-dimensional GEI data. Finally the support vector machine (SVM) models are trained by the decomposed feature vectors, and the gaits are classified by the trained SVM models at last. Experimental results show that the proposed feature extraction method is efficient in reducing computational complexity and preserving image information.

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.

In this paper we address the problem of selecting the most relevant features for human identification by gait. Although gait as a behavioral biometric is concerned with how people walk rather than how people look, most existing gait recognition approaches employ both shape and dynamics information for recognition. This is because shape, as a static appearance feature also contains useful information for identification. However, the inclusion of shape information in the gait features can also introduce variations that will hinder the recognition performance. To address this problem, we develop both supervised and unsupervised feature selection methods to extract the most relevant and informative features from Gait Energy Image (GEI) for human identification. Extensive experiments are carried out which indicate that our feature selection methods significantly improve the performance of gait recognition.

Tensor Rank One Discriminant Analysis—A convergent method for discriminative multilinear subspace selection

This paper reports the scaling behavior of the fuzzy c-means based classifier (FCMC) with many parameters. FCMC is a classifier based on clustering approaches. The classification accuracy on test sets (i.e., the generalization capability) is not necessarily improved by increasing the number of clusters. Especially when the number of training samples is relatively small, not only the classification boundary over-fits the data, but also covariance matrices and cluster centers are computed incorrectly, since the number of samples in each cluster becomes smaller. Hence, the test set accuracy deteriorates. The performance of FCMC with two clusters in each class and the number of training samples less than 1000, was reported in the literature. This paper reports the scaling behavior of FCMC by testing with variously-sized training samples. The number of clusters of FCMC is increased up to eight. The number of clusters used in this paper is not very large but the number of parameters is relatively large. So, the parameters are optimized to training sets. LibSVM is one of the widely known state of the art tools for support vector machines (SVM). The test set accuracy, training time and testing time (i.e., the detection time) of FCMC are compared with LibSVM by varying the size of training sets. FCMC shows a good generalization capability, though the parameters are optimized to training sets. When the number of training samples is increased by 10 times, the training time of FCMC increases by 10 times, but that of LibSVM increases by a factor of 100. The testing time is also much shorter than LibSVM when the size of the training set is large.