Gait Recognition System: A Survey

Human identification by gait has created a great deal of interest in computer vision community due to its advantage of inconspicuous recognition at a relatively far distance. This paper provides a comprehensive survey of recent developments on gait recognition approaches. The survey emphasizes on three major issues involved in a general gait recognition system, namely gait image representation, feature dimensionality reduction and gait classification. It also gives, a review of the available public gait datasets. The concluding discussions outline a number of research challenges and provide promising future directions for the field. In this article presents a survey of the work done in gait analysis for re-identification in the past decade, looking at the main approaches, datasets, and evaluation methodologies also discussed.

An Efficient Communication Intrusion Detection Scheme in AMI Combining Feature Dimensionality Reduction and Improved LSTM

Communication intrusion detection in Advanced Metering Infrastructure (AMI) is an eminent security technology to ensure the stable operation of the Smart Grid. However, methods based on traditional machine learning are not appropriate for learning high-dimensional features and dealing with the data imbalance of communication traffic in AMI. To solve the above problems, we propose an intrusion detection scheme by combining feature dimensionality reduction and improved Long Short-Term Memory (LSTM). The Stacked Autoencoder (SAE) has shown excellent performance in feature dimensionality reduction. We compress high-dimensional feature input into low-dimensional feature output through SAE, narrowing the complexity of the model. Methods based on LSTM have a superior ability to detect abnormal traffic but cannot extract bidirectional structural features. We designed a Bi-directional Long Short-Term Memory (BiLSTM) model that added an Attention Mechanism. It can determine the criticality of the dimensionality and improve the accuracy of the classification model. Finally, we conduct experiments on the UNSW-NB15 dataset and the NSL-KDD dataset. The proposed scheme has obvious advantages in performance metrics such as accuracy and False Alarm Rate (FAR). The experimental results demonstrate that it can effectively identify the intrusion attack of communication in AMI.

Intelligent predicting reaction performance in multi-dimensional chemical space using quantile regression forest

Buchwald-Hartwig amination reaction is widely applied in synthetic organic chemistry, which faces tedious and complex experimental process. In 2018, an interesting yield prediction technique is proposed via machine learning (random forest) in Science. However, the method is based on point prediction with many feature descriptors. For tackling these problems, complements and improvements have been made from the perspectives of machine learning and statistics, including feature dimensionality reduction, distributed prediction and visualization, so as to provide accurate and reliable decision information.

Image Identification Algorithm of Deep Compensation Transformation Matrix based on Main Component Feature Dimensionality Reduction

In order to reconstruct and identify three-dimensional (3D) images, an image identification algorithm based on a deep learning compensation transformation matrix of main component feature dimensionality reduction is proposed, including line matching with point matching as the base, 3D reconstruction of point and line integration, parallelization automatic differentiation applied to bundle adjustment, parallelization positive definite matrix system solution applied to bundle adjustment, and an improved classifier based on a deep compensation transformation matrix. Based on the INRIA database, the performance and reconstruction effect of the algorithm are verified. The accuracy rate and success rate are compared with L1APG, VTD, CT, MT, etc. The results show that random transformation and re-sampling of samples during training can improve the performance of the classifier prediction algorithm under the condition that the training time is short. The reconstructed image obtained by the algorithm described in this study has a low correlation with the original image, with high number of pixels change rate (NPCR) and unified average changing intensity (UACI) values and low peak signal to noise ratio (PSNR) values. Image reconstruction effect is better with image capacity advantage. Compared with other algorithms, the proposed algorithm has certain advantages in accuracy and success rate with stable performance and good robustness. Therefore, it can be concluded that image recognition based on the dimension reduction of principal component features provides good recognition effect, which is of guiding significance for research in the image recognition field.

Biometric Identification from Human Aesthetic Preferences

In recent years, human–machine interactions encompass many avenues of life, ranging from personal communications to professional activities. This trend has allowed for person identification based on behavior rather than physical traits to emerge as a growing research domain, which spans areas such as online education, e-commerce, e-communication, and biometric security. The expression of opinions is an example of online behavior that is commonly shared through the liking of online images. Visual aesthetic is a behavioral biometric that involves using a person’s sense of fondness for images. The identification of individuals using their visual aesthetic values as discriminatory features is an emerging domain of research. This paper introduces a novel method for aesthetic feature dimensionality reduction using gene expression programming. The proposed system is capable of using a tree-based genetic approach for feature recombination. Reducing feature dimensionality improves classifier accuracy, reduces computation runtime, and minimizes required storage. The results obtained on a dataset of 200 Flickr users evaluating 40,000 images demonstrate a 95% accuracy of identity recognition based solely on users’ aesthetic preferences.