Real-time eyeglass detection using transfer learning for non-standard facial data

Abstract

The aim of this paper is to build a real-time eyeglass detection framework based on deep features present in facial or ocular images, which serve as a prime factor in forensics analysis, authentication systems and many more. Generally, eyeglass detection methods were executed using cleaned and fine-tuned facial datasets; it resulted in a well-developed model, but the slightest deviation could affect the performance of the model giving poor results on real-time non-standard facial images. Therefore, a robust model is introduced which is trained on custom non-standard facial data. An Inception V3 architecture based pre-trained convolutional neural network (CNN) is used and fine-tuned using model hyper-parameters to achieve a high accuracy and good precision on non-standard facial images in real-time. This resulted in an accuracy score of about 99.2% and 99.9% for training and testing datasets respectively in less amount of time thereby showing the robustness of the model in all conditions.