There is a significant interest in facial emotion recognition in the fields of human–computer interaction and social sciences. With the advancements in artificial intelligence (AI), the field of human behavioral prediction and analysis, especially human emotion, has evolved significantly. The most standard methods of emotion recognition are currently being used in models deployed in remote servers. We believe the reduction in the distance between the input device and the server model can lead us to better efficiency and effectiveness in real life applications. For the same purpose, computational methodologies such as edge computing can be beneficial. It can also encourage time-critical applications that can be implemented in sensitive fields. In this study, we propose a Raspberry-Pi based standalone edge device that can detect real-time facial emotions. Although this edge device can be used in variety of applications where human facial emotions play an important role, this article is mainly crafted using a dataset of employees working in organizations. A Raspberry-Pi-based standalone edge device has been implemented using the Mini-Xception Deep Network because of its computational efficiency in a shorter time compared to other networks. This device has achieved 100% accuracy for detecting faces in real time with 68% accuracy, i.e., higher than the accuracy mentioned in the state-of-the-art with the FER 2013 dataset. Future work will implement a deep network on Raspberry-Pi with an Intel Movidious neural compute stick to reduce the processing time and achieve quick real time implementation of the facial emotion recognition system.
Purpose
Synthetic aperture radar (SAR) imaging is the most computational intensive algorithm and this makes its implementation challenging for real-time application. This paper aims to present the chirp-scaling algorithm (CSA) for real-time SAR applications, using advanced field programmable gate array (FPGA) processor.
Design/methodology/approach
A chirp signal is generated and compressed using range Doppler algorithm in MATAB for validation. Fast Fourier transform (FFT) and multiplication operations with complex data types are the major units requiring heavy computation. Therefore, hardware acceleration is proposed and implemented on NEON-FPGA processor using NE10 and CEPHES library.
Findings
The heuristic analysis of the algorithm using timing analysis and resource usage is presented. It has been observed that FFT execution time is reduced by 61% by boosting the performance of the algorithm and speed of multiplication operation has been doubled because of the optimization.
Originality/value
Very few literatures have presented the FPGA-based SAR imaging implementation, where analysis of windowing technique was a major interest. This is a unique approach to implement the SAR CSA using a hybrid approach of hardware–software integration on Zynq FPGA. The timing analysis propagates that it is suitable to use this model for real-time SAR applications.
Real-Time Facial Emotion Recognition Framework for Employees of Organizations Using Raspberry-Pi
