Despite considerable progress in face recognition technology in recent years, deep learning (DL) and convolutional neural networks (CNN) have revealed commendable recognition effects with the advent of artificial intelligence and big data. FaceNet was presented in 2015 and is able to significantly improve the accuracy of face recognition, while also being powerfully built to counteract several common issues, such as occlusion, blur, illumination change, and different angles of head pose. However, not all hardware can sustain the heavy computing load in the execution of the FaceNet model. In applications in the security industry, lightweight and efficient face recognition are two key points for facilitating the deployment of DL and CNN models directly in field devices, due to their limited edge computing capability and low equipment cost. To this end, this paper provides a lightweight learning network improved from FaceNet, which is called FN13, to break through the hardware limitation of constrained computational resources. The proposed FN13 takes the advantage of center loss to reduce the variations of the between-class features and enlarge the difference of the within-class features, instead of the triplet loss by using FaceNet. The resulting model reduces the number of parameters and maintains a high degree of accuracy, only requiring few grayscale reference images per subject. The validity of FN13 is demonstrated by conducting experiments on the Labeled Faces in the Wild (LFW) dataset, as well as an analytical discussion regarding specific disguise problems.

In this paper, we propose a method for improving the robustness of real-time facial expression recognition. Although there are many ways to improve the accuracy of facial expression recognition, a revamp of the training framework and image preprocessing allow better results in applications. One existing problem is that when the camera is capturing images in high speed, changes in image characteristics may occur at certain moments due to the influence of light and other factors. Such changes can result in incorrect recognition of the human facial expression. To solve this problem for smooth system operation and maintenance of recognition speed, we take changes in image characteristics at high speed capturing into account. The proposed method does not use the immediate output for reference, but refers to the previous image for averaging to facilitate recognition. In this way, we are able to reduce interference by the characteristics of the images. The experimental results show that after adopting this method, overall robustness and accuracy of facial expression recognition have been greatly improved compared to those obtained by only the convolution neural network (CNN).

A vision-based three degree-of-freedom translational parallel manipulator (TPM) was developed. The developed TPM has the following characteristics. First, the TPM is driven by three rodless pneumatic actuators and is designed as a horizontal structure to enlarge its horizontal working space to cover a conveyor. Then, a robot-vision system (including a webcam mounted on the TPM) collects images of objects on the conveyor and transfers them through the LabVIEW application programming interface for image processing. Since it is very difficult to achieve precise position control of the TPM due to the nonlinear couplings among the robot axes, feedback linearization is utilized to design an adaptive interval type-2 fuzzy controller with self-tuning fuzzy sliding-mode compensation (AIT2FC-STFSMC) for each rodless pneumatic actuator to attenuate nonlinearities, function approximation errors, and external disturbances. Finally, experiments proved that the vision-based three degree-of-freedom TPM was capable of accurately tracking desired trajectories and precisely executing pick-and-place movement in real time.

This paper focuses on developing a pneumatic-driven and horizontal-structure linear delta robot (PH-LDR) and increasing its trajectory tracking performance on the three-degrees-of-freedom (3-DOF) space. With the investigation of inverse and forward kinematics, the parallel mechanism of PH-LDR is designed by using three high-power and low-cost rod-less pneumatic actuators (PAs) to track 3-DOF motion, and this has a horizontal structure to enlarge the workspace. Since the PH-LDR features nonlinear coupling among its three axes and is disturbed by the three high-nonlinear rod-less PAs, the tracking control performance is significantly decreased, subject to uncertain nonlinearity and parametric uncertainty. Therefore, a fuzzy-PID controller is used to achieve highly accurate 3-DOF trajectory tracking, and furthermore, this study exploits neural networks (NNs) to pre-compensate the impacts arising from the compressibility of air and temperature change. The control system for the PH-LDR also features an embedded controller that allows real-time control. Experimental demonstration verifies the developed PH-LDR with the proposed controller, and the dynamic tracking accuracy in 3-DOF trajectory can be achieved.