Hardware Accelerator for Facial Expression Classification Using Linear SVM

In this paper, we present hardware accelerator for Facial Expression Classification using One-Versus-All (OVA) linear Support Vector Machine (SVM) classifier. The motivation behind this work is to perform real-time classification of facial expressions into three different classes: neutral, happy and pain, which could be used in an embedded system to facilitate automatic patient monitoring in ICUs of hospitals without any personal assistance. Pipelining and parallelism (inherent qualities of FPGAs) have been utilized in our architecture to achieve optimal performance. For achieving high accuracy, the architecture has been designed using IEEE-754 single precision floating-point data format. We performed the SVM training offline and used the trained parameters to implement its testing part on Field Programmable Gate Array (FPGA). Synthesis result shows that the designed architecture is operating at a maximum clock frequency of 200 MHz. Classification accuracy of 97.87% has been achieved on simulating the design with different test images. Thus, the designed architecture of the OVA linear SVM shows good performance in terms of both speed and accuracy facilitating real-time classification of the facial expressions.