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Recently, demand for biometric access controls and online payments in smartphones increased, necessitating further investigation and development in this area. This article proposes a new low-cost palm vein recognition system for smartphones using red, green, blue (RGB) images. First, we detect and enhance palm vein patterns, using the saturation channel instead of the red channel as in the existing approaches. Then, to address the challenging contactless capturing problems of smartphones—such as scale variants, rotation, closed fingers, or rings on hand—we introduce an improved method for the region of interest extraction, based on the convex hull, with a new idea for key vector use. We also designed a new lightweight deep learning-based model for smartphones, which was overlooked in previous palm vein recognition studies. The proposed model comprises suitable blocks of convolution, depthwise separable convolution, inverted residual bottleneck, and spatial pyramid pooling module; in addition, the accuracy is enhanced with fusion strategy. Results show that the proposed model is both smaller and more accurate than related models. The integrated proposed model obtains the best equal error rate, 0.49%, and an inference time of 8 ms. [ABSTRACT FROM AUTHOR]

Labor is the most expensive in retail stores. In order to increase the profit of retail stores, unmanned stores could be a solution for reducing labor cost. Deep learning is a good way for recognition, classification, and so on; in particular, it has high accuracy and can be implemented in real time. Based on deep learning, in this paper, we use multiple deep learning models to solve the problems often encountered in unmanned stores. Instead of using multiple different sensors, only five cameras are used as sensors to build a high-accuracy, low-cost unmanned store; for the full use of space, we then propose a method for calculating stacked goods, so that the space can be effectively used. For checkout, without a checking counter, we use a Siamese network combined with the deep learning model to directly identify products instantly purchased. As for protecting the store from theft, a new architecture was proposed, which can detect possible theft from any angle of the store and prevent unnecessary financial losses in unmanned stores. As all the customers' buying records are identified and recorded in the server, it can be used to identify the popularity of the product. In particular, it can reduce the stock of unpopular products and reduce inventory. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) were popular in ImageNet large scale visual recognition competition (ILSVRC 2012) because of their identification ability and computational efficiency. This paper proposes a palm vein recognition method based on CNN. The four main steps of palm vein recognition are image acquisition, image preprocessing, feature extraction, and matching. To reduce the processing steps in the recognition of palm vein images, a palm vein recognition method using a CNN is proposed. CNN is a deep learning network. Palm vein images are acquired using near-infrared light, under which the veins in the palm of the hand are relatively prominent. To obtain a good vein image, many previous methods used preprocessing to further enhance the image before using feature extraction to find feature matches for further comparison. In recent years, CNNs have been shown to have great advantages and have performed well in image classification. To reduce early-stage image processing, a CNN is used to classify and recognize palm vein images. The networks AlexNet and VGG depth CNN were trained to extract image features. The palm vein recognition rates by VGG-19, VGG-16, and AlexNet were 98.5%, 97.5%, and 96%, respectively. [ABSTRACT FROM AUTHOR]

Air quality forecasting has been regarded as the key problem of air pollution early warning and control management. In this article, we propose a novel deep learning model for air quality (mainly PM2.5) forecasting, which learns the spatial-temporal correlation features and interdependence of multivariate air quality related time series data by hybrid deep learning architecture. Due to the nonlinear and dynamic characteristics of multivariate air quality time series data, the base modules of our model include one-dimensional Convolutional Neural Networks (1D-CNNs) and Bi-directional Long Short-term Memory networks (Bi-LSTM). The former is to extract the local trend features and spatial correlation features, and the latter is to learn spatial-temporal dependencies. Then we design a jointly hybrid deep learning framework based on one-dimensional CNNs and Bi-LSTM for shared representation features learning of multivariate air quality related time series data. We conduct extensive experimental evaluations using two real-world datasets, and the results show that our model is capable of dealing with PM2.5 air pollution forecasting with satisfied accuracy. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of Facial Expression Recognition (FER), focusing on unobvious facial movements. Traditional methods often cause overfitting problems or incomplete information due to insufficient data and manual selection of features. Instead, our proposed network, which is called the Multi-features Cooperative Deep Convolutional Network (MC-DCN), maintains focus on the overall feature of the face and the trend of key parts. The processing of video data is the first stage. The method of ensemble of regression trees (ERT) is used to obtain the overall contour of the face. Then, the attention model is used to pick up the parts of face that are more susceptible to expressions. Under the combined effect of these two methods, the image which can be called a local feature map is obtained. After that, the video data are sent to MC-DCN, containing parallel sub-networks. While the overall spatiotemporal characteristics of facial expressions are obtained through the sequence of images, the selection of keys parts can better learn the changes in facial expressions brought about by subtle facial movements. By combining local features and global features, the proposed method can acquire more information, leading to better performance. The experimental results show that MC-DCN can achieve recognition rates of 95%, 78.6% and 78.3% on the three datasets SAVEE, MMI, and edited GEMEP, respectively. [ABSTRACT FROM AUTHOR]

Time series forecasting is an important technique to study the behavior of temporal data and forecast future values, which is widely applied in many fields, e.g. air quality forecasting, power load forecasting, medical monitoring, and intrusion detection. In this paper, we firstly propose a novel temporal attention encoder–decoder model to deal with the multivariate time series forecasting problem. It is an end-to-end deep learning structure that integrates the traditional encode context vector and temporal attention vector for jointly temporal representation learning, which is based on bi-directional long short-term memory networks (Bi-LSTM) layers with temporal attention mechanism as the encoder network to adaptively learning long-term dependency and hidden correlation features of multivariate temporal data. Extensive experimental results on five typical multivariate time series datasets showed that our model has the best forecasting performance compared with baseline methods. [ABSTRACT FROM AUTHOR]