Research On Iris Detection And Eye Movement Data Classification Based On Deep Learning

To a large extent,humans rely on the human eye to obtain external information.Through the analysis of the point of view of the human eye,human interest points can be obtained,thereby obtaining information of human mental activities.Due to the importance of the human eye,many eye movement devices that use the human eye as a medium have been produced.It has broad development prospects in the fields of clinical medical research,psychological research,and educational research.Iris positioning and eye movement data classification are the key technologies to realize eye movement equipment.Therefore,this paper analyzes and studies these two technologies and obtains good experimental results.The following points are the main contents and innovations of this article:(1)The existing iris detection based on traditional algorithms is less robust,while the deep learning-based algorithm has better detection results,but its calculation is large and the detection speed is slow.Aiming at this defect,this paper proposes a lightweight network structure DDSNet based on deep learning.This network uses a dense block structure,which can have a good detection effect when the number of convolution kernels is small.By using depth separable convolutions,the calculation of the model is further reduced.In order to improve the performance of the model,a data enhancement strategy is added in this paper to transform the pictures of the training set to increase its size and improve the robustness of the model.The structure of this paper is evaluated on the iris data set.Compared with other lightweight networks,the speed of the model is significantly improved,and the storage capacity is significantly reduced.The model can detect 639 pictures in 1 second under the GPU,the storage capacity is only 1.3M,and the F1 score reaches 99.95%.It can be seen that the model in this article has a good performance in speed,storage and accuracy.(2)In this paper,an eye movement classification algorithm based on deep learning is used.This method first extracts five features of different time scales,inputs them into one-dimensional convolution to extract internal characteristics,and uses BLSTM to obtain the relationship between the time series before and after the data set.After testing on the Gaze Com eye movement data set,it was found that the F1 score of the smooth pursuit point was low,so the algorithm was improved in the following way.The smooth pursuit points and saccade points of minority samples are enhanced by resampling and artificial synthesis to increase the data volume of minority samples.The loss function of focal loss is used to increase the learning weight of difficult samples and strengthen the learning of difficult samples.Attention module is added to the network structure to recalibrate the features and strengthen the important features.Through these improvements,the model has a better classification ability,and the F1 scores of the fixation point,smooth pursuit point and saccade point are increased by 1.2%,3.5% and 2.4%,respectively.