Hybrid Facial Expression Analysis Model using Quantum Distance-based Classifier and Classical Support Vector Machine

Rapid advancements in image and video processing technologies are poised to create remarkable impacts on a wide range of industries. A significant challenge in these processing technologies resides in identifying the features fed for image classification algorithms. Though all classification algorithms could identify, extract and classify the features of a given image, their accuracy is directly proportional to the number of sample points taken from the image using a sampling technique. As the accuracy improves with a substantial number of sample points, the time consumed to process them looms large. These challenges beseech enormous computing power. Quantum computers avowed exceptional computing power is expected to bridge the growing demands. To address these challenges effectively, we have chosen a specific problem, Facial Expression Analysis, to explore in-depth and arrive at a purposeful approach to deliver the desired outcome. The purpose of this paper is two-pronged. Perform a comparative study of accuracy and performance of classical and quantum image processing algorithms in classical and quantum computers, respectively. Secondly, devise a novel hybrid model using a quantum distance-based classifier augmented with a classical linear support vector machine to overcome the limitations observed. Sample image features derived from the quantum classifier were used to train the linear classifier. The results were observed to be better relative to results from the classical distance-based classifier. Holistically, the novel hybrid model is observed as a promising solution for all image classification problems. Our future work will focus on sophisticated usage of a linear classification algorithm in quantum computing.