Smart factories require secure and sustainable worker authentication for safe operations. Biometric continuous authentication based on facial recognition is one of the most convenient mechanisms. This method applies a face recognition task to verify the captured face as an authorized user. However, existing methods that employ large networks for high-accuracy face recognition incur high computational costs and slow down the process, rendering them unsuitable for continuous operation. This work proposes an efficient and rapid face recognizer with high accuracy. It offers a faster face residual network, containing efficient FasterFace blocks and efficient channel spatial attention for improved feature extraction. As a result, the proposed network achieves 97.08% based on average accuracy, outperforming the other networks on five benchmark datasets. It performs faster at 19.91 frames per second in real time on CPU-based hardware when integrated with a face detector, showcasing its capacity to support real-time biometric continuous authentication for smart factory workers.

Traffic accidents are the leading death rate among accident categories. One of the major causes of road traffic accidents is driver drowsiness. Many studies have paid attention to this issue and developed driver assistance tools to reduce the risk. These methods mainly analyze driver behavior, vehicle behavior, and driver physiology. This article proposes a driver eye status surveillance system based on lightweight convolutional neural networks (CNNs). The overall system consists of the following three stages: Face detection, eye detection, and eye classification. In the first stage, the system utilizes a small real-time face detector, named nano YOLO5Face. The second stage focuses on exploiting the compact CNN network architecture combined with the inception network, and triplet attention mechanism. Finally, the system uses a simple classification network architecture to classify open or closed eye status. Additionally, this work also provides the datasets for the eye detection task comprised of 10 659 images and 21 318 labels. As a result, the real-time testing reached 33.12 frames per second (FPS) and 25.11 FPS on an Intel Core I7-4770 CPU @ 3.40 GHz [personal computer (PC)] and a 128-core Nvidia Maxwell GPU (Jetson Nano device), respectively.

Object detection is an important study in computer vision to discriminate the position and class of an object in an image. Object detection in drone images is a technology that automatically detects and classifies objects using deep learning algorithms in flight images taken by drones. Object detection using drone images can rescue human life in disaster situations, grasp the situation at the disaster site, and identify the growth status of crops or pests in agriculture. In addition, it can be used in various fields such as infrastructure management, roads and railways, and city planning. A quick calculation is required. Although rapid computation is possible due to recent hardware development, there are many difficulties in using GPUs in industrial settings. In order to utilize drones in industrial sites, an object detection algorithm capable of real-time operation in a low-cost device is required. In this paper, we propose YOLOv5 with the combination of Coordinate Attention and CBAM for Object Detection on Drone for an algorithm capable of real-time operation in a low-cost device. The proposed architecture makes the model lighter by reducing the number of parameters and improves the object detection rate of the model through Coordinate Attention and CBAM. The model is trained using the VisDrone dataset, and the object detection rate, mAP, increased by about 10% to 22.2mAP, and the number of parameters decreased by about 70% to 2,147,589.

In modern cities, intelligent advertising platforms have been widely engaged in public areas. A facial attribute recognition technique is essential to assist these platforms in delivering suitable adverts for each audience. These platforms also require a recognition technology that can operate at least suitably on a CPU device to reduce implementation costs. This work proposed a lightweight multi-label CNN-Transformer architecture with an efficient inception block (EIB) and squeeze channel transformer encoder (SCTE) to perform facial attribute recognition efficiently. EIB is used to extract face features in multi-scale and levels supported by SCTE in improving its feature map's quality. The proposed architecture produces fewer parameters with low operations and gains competitive accuracy on the CelebA and LWFA datasets consisting of images with multi-label. Moreover, the proposed architecture integrated with face detection can perform sufficiently on a CPU configuration in real-time with 21 frames per second (FPS) using 224 × 224 input size of face area image.

Facial expression detection is a method to predict human facial emotions. This work is a trending research topic that can be implemented for human-robot interaction. More recently, deep convolutional neural network provides a robust extractor features but tends to be slow in real-time implementations and often requires a large memory and graphics processing units for fast execution. In this article, an efficient CPU-based facial expression detector is proposed using a sequential attention network to improve the baseline performance. The proposed attention network consists of three modules, global representation to capture the global features, channel representation, and dimension representation, which are focused on the channel and using spatial attention to discriminate local features. The efficient partial transfer module is also presented as a light backbone to extract facial features from an image. The entire module is trained and tested on several benchmarks to classify seven facial expressions. As a result, the proposed model reaches an accuracy of 98.18%, 98.75%, 95.63%, and 74.17% on CK+, JAFFE, KDEF, and FER-2013, respectively. It achieves competitive performance when compared to state-of-the-art methods. Lastly, it is integrated with a face detector and runs in real-time without a constraint at 69 frames per second on a CPU.

Driving is an activity that requires the coordination of many senses with complex manipulations. However, the driver can be affected by a several factors such as using a mobile phone, adjusting audio equipment, smoking, drinking, eating, talking to a passenger or drowsy. Therefore, the development of assistant applications to warn distracted driver is very necessary. Because of the limited space and mobility, the equipment also requires compact, energy-saving and efficient. This paper proposes a lightweight Convolutional Neural Network for a distracted driver warning system. The method is built based on a combination of standard convolution and Depthwise Separable Convolution operation to optimize the network parameters but still ensure the important information and speed. The network was trained and evaluated on two datasets, AUC (the American University in Cairo) and StateFarm dataset from Kaggle’s competition. As a result, the evaluation accuracy reached 95.36% and 99.95%, respectively.

Face detection is crucial in the development of face recognition, expression, tracking, and classification. Conventional methods have accuracy constraints on several challenging conditions, including nonfrontal faces, occlusions, and complex backgrounds. However, the convolutional neural network (CNN) methods produce high performances despite a large amount of computation. Therefore, CNN requires expensive hardware and is not suitable for low-cost central processing units (CPUs). This article develops a light architecture for a CNN-based real-time face detector. The proposed architecture consists of two main modules, the backbone to extract distinctive facial features and multilevel detection to perform prediction at multiple scales. Furthermore, it utilizes several approaches to enhance the training result, including balancing loss and tweaks on the training configuration. The proposed detector has one stage and is trained using the input of images from WIDER FACE with challenges, which contains more challenging images than other datasets. As a result, the detector achieves state-of-the-art performance on several benchmark datasets compared with the other CPU-based models. Then, its efficiency is superior to that of competitors, as it runs at 53 frames per second on a CPU for video graphics array resolution images.

Face detection plays an essential role in the success of the interaction between service robots and consumers. This method is the initial stage for face-related applications. Practical applications require face detection to work in real-time and can be implemented on low-cost devices such as CPU. Traditional methods have problems when the face is not frontal, blocked, and partially covered, but real-time speed is not an obstacle. On the other hand, deep learning has succeeded in accurately distinguishing facial features and backgrounds. Face sizes that tend to be medium and large when robot interaction with consumers so it can employ Convolutional Neural Networks (CNN) with light weights. In this paper, a real-time face detector is built that can work on the CPU. This detector will be implemented explicitly in service robots to support interactions with consumers. It can overcome the occlusion and not-frontal face. Detector architecture consists of the backbone as rapidly features extractor, transition module as a transformer of prediction map, and the dual-detection layer is head of a network prediction based on scale assignment. As a result, the detector can work at speeds of 301 frames per second on CPU without ignoring the accuracy.

The drowsiness is the leading cause of many accidents on the road. These causes can be reduced by using the drowsiness alarm or drowsiness detection system. These systems monitor drivers while driving and alarm when they don't focus or have some abnormal signs in the driver's body. Currently, most methodologies use the analysis of human behaviors, vehicle behaviors, and human physiological conditions. This paper regards eyes status analysis based on deep learning method using proposed Convolutional Neural Networks (CNN) with two stages are face detection and eyes classification. The face detector employs a single detector module and shallow layer, then the eyes classifier using simple CNN without ignoring the accuracy. As a result, the average speed was tested in real-time by 50.03 fps (frames per second) on Intel Core I7-4770 CPU @ 3.40 GHz.

In this paper, a real-time face expression based on a Convolutional Neural Network with a Dual Attention Module is presented for classifying various facial emotions. The system contains two main components. Firstly, local convolutional features of faces are extracted by the VGG13 baseline. Secondly, dual attention masks are automatically employed to enhance the backbone end based on the global probability of features. It represents the position and channel of the feature map. The local features from baseline are combined with the attention to infer the emotional label module. A single network is trained in an end-to-end scheme with five million parameters. Experiments on benchmark datasets show the attention module gives increased accuracy. Besides, this module provides lightweight and runs 60.20 frames per second when working in real-time on CPU devices.