Quantum Shift Based Gaze Pattern Recognition Using Recurrent Neural Machine Learning Technique

Abstract

Gaze pattern recognition is the technique for detecting what and where the human eyes are pointing in a predefined plane. It is important for predicting human attention and also used to recognize human activities along with interactive systems. Several techniques are introduced in the field of human vision identification with a number of patterns. These methods achieve fewer eyes tracking quality and it takes more time complexity (TC) for recognition.  A new technique called Quantum Shift Based Recurrent Neural Machine Learning (QS-RNML) technique is developed for efficient gaze pattern recognition with minimum time. At first, the number of eye images is taken from the Dataset (DS). QS-RNML technique includes three processes namely preprocessing, gaze estimation and pattern recognition. The Adaptive median filter is employed in preprocessing of eye images for removing the noise artifacts from eye images. In gaze estimation process, Quantum Shift is applied to estimate the patterns by movement of the eyelid in gaze plane. After that, Recurrent Neural Machine Learning technique is used for recognizing the gaze patterns by matching the estimated patterns with the ground truth patterns.  The proposed QS-RNML technique is simple and efficient for identifying the human visual attention, emotion, feelings and so on. Simulation results of proposed QS-RNML technique are carried out using Synthes eyes DS and MPII gaze DS. The results evident that QS-RNML technique improves gaze pattern recognition accuracy (GPRA), true positive rate (TPR) and lessens the TC as well as false positive rate(FPR).