Gait recognition aims to identify people by their walking patterns. The normal human walking is a periodic movement. However, the existing gait recognition methods rarely make use of the gait periodicity. In this paper, we propose the gait Periodicity-inspired Temporal feature Pyramid aggregator (PTP) that introduces gait periodicity priors into the gait feature extraction, resulting in a strong and robust skeleton-based gait recognition method called CycleGait. Specially, inspired by gait periodicity, PTP adopts multiple parallel temporal convolution operators with pyramid temporal kernel sizes to extract gait temporal features. Then PTP cooperates with spatial Graph Convolutional Network (GCN) to form the GCN-PTP network. CycleGait uses this network to extract spatio-temporal gait features from the sequence of skeleton coordinates. Besides, to make CycleGait more robust and have better performance, we feed more gait samples with various gait cycles into CycleGait by the plug-and-play Irregular Pace Converter (IPC) that can automatically convert normal pace into irregular and reasonable pace. Extensive experiments conducted on CASIA-B dataset and OG RGB+D dataset show that CycleGait has excellent performance in various complex scenarios, namely, cross-view and cross-walking-condition, and becomes one of the best SOTA methods, which not only outperforms the existing best gait recognition methods by a large margin, but also exhibits a significant level of robustness.

Traditional eye movement models are based on psychological assumptions and empirical data that are not able to simulate eye movement on previously unseen text data. To address this problem, a new type of eye movement model is presented and tested in this paper. In contrast to conventional psychology‐based eye movement models, ours is based on a recurrent neural network (RNN) to generate a gaze point prediction sequence, by using the combination of convolutional neural networks (CNN), bidirectional long short‐term memory networks (LSTM), and conditional random fields (CRF). The model uses the eye movement data of a reader reading some texts as training data to predict the eye movements of the same reader reading a previously unseen text. A theoretical analysis of the model is presented to show its excellent convergence performance. Experimental results are then presented to demonstrate that the proposed model can achieve similar prediction accuracy while requiring fewer features than current machine learning models.

This paper brings forth a learning-based visual saliency model method for detecting diagnostic diabetic macular edema (DME) regions of interest (RoIs) in retinal image. The method introduces the cognitive process of visual selection of relevant regions that arises during an ophthalmologist’s image examination. To record the process, we collected eye-tracking data of 10 ophthalmologists on 100 images and used this database as training and testing examples. Based on analysis, two properties (Feature Property and Position Property) can be derived and combined by a simple intersection operation to obtain a saliency map. The Feature Property is implemented by support vector machine (SVM) technique using the diagnosis as supervisor; Position Property is implemented by statistical analysis of training samples. This technique is able to learn the preferences of ophthalmologist visual behavior while simultaneously considering feature uniqueness. The method was evaluated using three popular saliency model evaluation scores (AUC, EMD, and SS) and three quality measurements (classical sensitivity, specificity, and Youden’sJstatistic). The proposed method outperforms 8 state-of-the-art saliency models and 3 salient region detection approaches devised for natural images. Furthermore, our model successfully detects the DME RoIs in retinal image without sophisticated image processing such as region segmentation.

Humans can easily classify different kinds of objects whereas it is quite difficult for computers. As a hot and difficult problem, objects classification has been receiving extensive interests with broad prospects. Inspired by neuroscience, deep learning concept is proposed. Convolutional neural network (CNN) as one of the methods of deep learning can be used to solve classification problem. But most of deep learning methods, including CNN, all ignore the human visual information processing mechanism when a person is classifying objects. Therefore, in this paper, inspiring the completed processing that humans classify different kinds of objects, we bring forth a new classification method which combines visual attention model and CNN. Firstly, we use the visual attention model to simulate the processing of human visual selection mechanism. Secondly, we use CNN to simulate the processing of how humans select features and extract the local features of those selected areas. Finally, not only does our classification method depend on those local features, but also it adds the human semantic features to classify objects. Our classification method has apparently advantages in biology. Experimental results demonstrated that our method made the efficiency of classification improve significantly.