Abstract Gait recognition has broad application prospects in intelligent security monitoring. However, due to the variability of human walking states and the complexity of external conditions during sample collection, gait recognition is still facing many challenges. Among them, gait recognition algorithms based on shallow learning are hard to achieve the correct recognition rate required by many applications, while the amount of gait training data cannot meet the needs of model training based on deep learning. To solve the above problem, this paper presents a novel gait recognition scheme based on sparse linear subspace. First, frame‐by‐frame gait energy images (ffGEIs) are extracted as primary gait features and sparse linear subspace technology is used to represent them for dimension reduction. Second, a new gait classification algorithm based on support vector machine is presented, which adopts Gaussian radial basis function (RBF) kernels to achieve cross‐view gait recognition. Finally, the proposed gait recognition approach is evaluated on two open‐accessed gait databases to demonstrate its performance.

The diversity of base classifiers and integration of multiple classifiers are two key issues in the field of ensemble learning. This paper puts forward a hybrid ensemble algorithm combining AdaBoost and genetic algorithm(GA) for cancer classification with gene expression data. The decision group is designed to increase the diversity of base classifier pool, and the GA is used to assign weight to each base classifier, thus to improve the classification performance by avoiding local extrema. The decision groups composed by using base classifiers, including K-nearest neighbor (KNN), Naïve Bayes (NB), and Decision Tree (C4.5). Experimental results show that the proposed algorithm is superior to those existing ensemble learning methods, such as Bagging, Random Forest (RF), Rotation Forest (RoF), AdaBoost, AdaBoost-BPNN, AdaBoost-SVM, and AdaBoost-RF, especially it has better performance on small samples and unbalanced gene expression data processing.

Human gait recognition has numerous challenges due to view angle changing, human dressing, bag carrying, and pedestrian walking speed, etc. In order to increase gait recognition accuracy under these circumstances, in this paper we propose a method for gait recognition based on a self-adaptive hidden Markov model (SAHMM). First, we present a feature extraction algorithm based on local gait energy image (LGEI) and construct an observation vector set. By using this set, we optimize parameters of the SAHMM-based method for gait recognition. Finally, the proposed method is evaluated extensively based on the CASIA Dataset B for gait recognition under various conditions such as cross view, human dressing, or bag carrying, etc. Furthermore, the generalization ability of this method is verified based on the OU-ISIR Large Population Dataset. Both experimental results show that the proposed method exhibits superior performance in comparison with those existing methods.

Human gait recognition is one of the most promising biometric technologies, especially for unobtrusive video surveillance and human identification from a distance. Aiming at improving recognition rate, in this paper we study gait recognition using deep learning and propose a novel method based on convolutional Long Short-Term Memory (Conv-LSTM). First, we present a variation of Gait Energy Images, i.e. frame-by-frame GEI (ff-GEI), to expand the volume of available Gait Energy Images (GEI) data and relax the constraints of gait cycle segmentation required by existing gait recognition methods. Second, we demonstrate the effectiveness of ff-GEI by analyzing the cross-covariance of one person's gait data. Then, making use of the temporality of our human gait, we design a novel gait recognition model using Conv-LSTM. Finally, the proposed method is evaluated extensively based on the CASIA Dataset B for cross-view gait recognition, furthermore the OU-ISIR Large Population Dataset is employed to verify its generalization ability. Our experimental results show that the proposed method outperforms other algorithms based on these two datasets. The results indicate that the proposed ff-GEI model using Conv-LSTM, coupled with the new gait representation, can effectively solve the problems related to cross-view gait recognition.

Gait recognition has been well known as a promising biometric, which is non‐offensive and can identify a person from a distance. In this study, a novel ensemble learning framework for gait recognition, namely multi‐perspective gait recognition based on classifier fusion is proposed. Firstly, by utilising bidirectional optical flow, a new algorithm for gait feature extraction is presented, which adaptively extracts the dynamic gait characteristics of walking persons. Secondly, two base classifiers, namely the support vector machine and the hidden Markov model, are trained using the extracted dynamic gait features and traditional gait energy images separately. Thirdly, a novel algorithm is presented for combining two types of base gait classifiers together on the decision level. Finally, the proposed framework by two experiments on the well‐known CASIA and OU‐ISIR gait databases is evaluated, respectively, and demonstrate the advantages of the proposed methods in comparison with others.

A large‐scale multi‐projector display system offers high‐resolution, high‐brightness and immersive visualisation for realistic experience to end users. It has been demonstrated to be effective tackling the conflict between the increasing demands of super‐resolution display and the resolution limitation of a single display system. However, there is still no standardisation method for curved‐surface projection screen. In this study, we propose a novel approach for calibrating multi‐projector display systems, which have curved surfaces. First, based on a detailed analysis on arbitrarily curved surfaces, we present a three‐dimensional reconstruction algorithm based on Bezier surface models. Then, for fully utilising the projection area of each projector, we propose a novel curved‐surface stitching algorithm to achieve geometry seamlessness of multi‐projector display systems. Experimental results show that by constructing local Bessel models for the curved screen, the proposed method performs better than traditional approaches, i.e. the new method achieves geometric calibration with higher accuracy. The proposed method of modelling projection screen and the corresponding automatic geometric correction scheme effectively increase the utilisation ratio of the original projection area of each projector and improve the calibration accuracy of multi‐projector system with continuous curved surface.