A model-based gait recognition method with body pose and human prior knowledge

As a unique biometric feature that can be recognized at a distance, gait has broad applications in crime prevention, forensic identification and social security. To portray a gait, existing gait recognition methods utilize either a gait template, where temporal information is hard to preserve, or a gait sequence, which must keep unnecessary sequential constraints and thus loses the flexibility of gait recognition. In this paper we present a novel perspective, where a gait is regarded as a set consisting of independent frames. We propose a new network named GaitSet to learn identity information from the set. Based on the set perspective, our method is immune to permutation of frames, and can naturally integrate frames from different videos which have been filmed under different scenarios, such as diverse viewing angles, different clothes/carrying conditions. Experiments show that under normal walking conditions, our single-model method achieves an average rank-1 accuracy of 95.0% on the CASIA-B gait dataset and an 87.1% accuracy on the OU-MVLP gait dataset. These results represent new state-of-the-art recognition accuracy. On various complex scenarios, our model exhibits a significant level of robustness. It achieves accuracies of 87.2% and 70.4% on CASIA-B under bag-carrying and coat-wearing walking conditions, respectively. These outperform the existing best methods by a large margin. The method presented can also achieve a satisfactory accuracy with a small number of frames in a test sample, e.g., 82.5% on CASIA-B with only 7 frames. The source code has been released at https://github.com/AbnerHqC/GaitSet.

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GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition

IDNet: Smartphone-based gait recognition with convolutional neural networks

Gait recognition is of great importance in the fields of surveillance and forensics to identify human beings since gait is the unique biometric feature that can be perceived efficiently at a distance. However, the accuracy of gait recognition to some extent suffers from both the variation of view angles and the deficient gait templates. On one hand, the existing cross-view methods focus on transforming gait templates among different views, which may accumulate the transformation error in a large variation of view angles. On the other hand, a commonly used gait energy image template loses temporal information of a gait sequence. To address these problems, this paper proposes multi-task generative adversarial networks (MGANs) for learning view-specific feature representations. In order to preserve more temporal information, we also propose a new multi-channel gait template, called period energy image (PEI). Based on the assumption of view angle manifold, the MGANs can leverage adversarial training to extract more discriminative features from gait sequences. Experiments on OU-ISIR, CASIA-B, and USF benchmark data sets indicate that compared with several recently published approaches, PEI + MGANs achieves competitive performance and is more interpretable to cross-view gait recognition.

Multi-Task GANs for View-Specific Feature Learning in Gait Recognition

The performance of gait recognition can be adversely affected by many sources of variation such as view angle, clothing, presence of and type of bag, posture, and occlusion, among others. In order to extract invariant gait features, we proposed a method named as GaitGAN which is based on generative adversarial networks (GAN). In the proposed method, a GAN model is taken as a regressor to generate invariant gait images that is side view images with normal clothing and without carrying bags. A unique advantage of this approach is that the view angle and other variations are not needed before generating invariant gait images. The most important computational challenge, however, is to address how to retain useful identity information when generating the invariant gait images. To this end, our approach differs from the traditional GAN which has only one discriminator in that GaitGAN contains two discriminators. One is a fake/real discriminator which can make the generated gait images to be realistic. Another one is an identification discriminator which ensures that the the generated gait images contain human identification information. Experimental results show that GaitGAN can achieve state-of-the-art performance. To the best of our knowledge this is the first gait recognition method based on GAN with encouraging results. Nevertheless, we have identified several research directions to further improve GaitGAN.

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GaitGAN: Invariant Gait Feature Extraction Using Generative Adversarial Networks

Complete canonical correlation analysis with application to multi-view gait recognition

The invention provides a method for tracking a moving object. The method comprises the following steps of 1, accurately detecting the moving object by a rapid moving object detection method based on a codebook model; 2, initializing a weak classifier group of an online Adaboost algorithm to obtain a strong classifier, fusing local direction histogram characteristics and color characteristic in selection of characteristics of the moving object; 3, computing a characteristic matrix of an online Adaboost tracking algorithm and the weak classifier to obtain a confidence chart, updating the weak classifier according to the obtained moving object position by a CamShift tracking algorithm on the confidence chart, and finally obtaining a tracking result of a whole segment of video sequence. The method for tracking the moving object capable of effectively tracking the moving object which largely deforms within a short period of time, generates shelter even large-area shelter, is similar to the colors of the other moving objects in background, rapidly changes and accelerates is provided by the invention.

Method for tracking moving object

Gait is an important biometric feature to identify a person at a distance. However, the performance of the traditional gait recognition methods may degenerate when the viewing angle is changed. This is because the viewing angle of the probe data may not be the same as the viewing angle under which the gait signature database is generated. In this paper, we introduce the separable criteria into the couple metric learning (CML) method, and apply this novel method to normalize gait features from various viewing angles into a couple feature spaces. Then, the gait similarity measurement is conducted in this common feature space. We incorporate the label information into the separable criteria to improve the performance of the traditional CML method. Experiments are performed on the benchmark gait database. The results demonstrate the efficiency of our method.

Erratum: Couple Metric Learning Based on Separable Criteria with Its Application in Cross-View Gait Recognition

Bionic prosthesis can help the disabled rebuild the athletic ability of their missing limbs. Biomimetic hand controlled by surface electromyography (sEMG) has been widely studied in recent years. However, a lot of researchers focus on the field of movement recognition, ignoring the precise control of grip speed and force in the design of the prosthesis. The study presents a new means to simulate the process of linear changes of grip force by using sEMG, which provides a simple and practicable way of precise control of the speed and strength of force for the myoelectric hand. We design a synchronous acquisition device for sEMG signal and grip force signal. A novel preprocessing methodology for the raw sEMG signal is introduced, which is adaptive filter based on the Normalized Least Mean Square (NLMS). Compared the traditional preprocessing method that uses a notch filter for removing power line interference (50 Hz), the presented method can effectively remove power line interference and its harmonic wave components, and it completely reserves useful information contained in the sEMG signal. The linear change process of grip force at five grip speeds are studied. A simple and efficient method for muscle contraction state detection is used. Six supervised classification algorithms are used to identify these grip patterns and achieve a highest recognition rate of over 99%. Five supervised regression algorithms are applied to predict the value of grip force and establish the mapping relationship between sEMG and force. The experimental results demonstate that the method can simulate the linear change process of grip force well, and it achieves a minimum standard deviation of 0.14272. 

Tao Yan

Papers

Complete canonical correlation analysis with application to multi-view gait recognition

Method for tracking moving object

Erratum: Couple Metric Learning Based on Separable Criteria with Its Application in Cross-View Gait Recognition

Classification and simulation of process of linear change for grip force at different grip speeds by using supervised learning based on sEMG

Overcoming the Effect of Muscle Fatigue on Gesture Recognition Based on Semg Via Generative Adversarial Networks