An AVOA-LSTM with MRCNN for segmenting and classifying the sunglass image-based eye region identification

Abstract

The recognition of the eye region from images is a challenging task, particularly when dealing with dark or thick sunglasses that cause reflections and interfere with accurate identification. To address this issue, a novel system called AVOA-MRCNN-OLSTM has been proposed. This system combines Optimization-driven Long Short-Term Memory (LSTM) with Mask RCNN to achieve precise eye recognition even in the presence of eyeglass frame interference. A mean histogram equalization approach is used in the system's first stage to eliminate noise, which improves the image quality. The system then uses Mask RCNN for segmentation and localization. A potent deep learning model called Mask RCNN can precisely recognize and isolate particular items inside an image. It is used in this instance to identify and divide the eye region. The AVOA-MRCNN-OLSTM framework makes use of LSTM, a recurrent neural network variety that can retain patterns for longer periods. It can efficiently acquire and use temporal information to increase eye recognition accuracy by integrating LSTM into the system. The proposed AVOA-MRCNN-OLSTM system's effectiveness is shown by experimental findings. It outperforms the performance of existing algorithms, achieving a remarkable accuracy of 99% in just 0.02 seconds of computing time. The potential uses of this development include biometric identity, surveillance systems, and human-computer interfaces, all of which need precise eye recognition.