Detailed Analysis of Blink Types Classification Using a 3D Convolutional Neural Network

In the development of blink input interfaces, it is important to classify between conscious (voluntary) and naturally occurring (involuntary) blinks. In the previous studies, some systems employed a long blink as a voluntary blink, but determining the appropriate discriminative condition was difficult. To avoid the problem of individual differences in discrimination conditions, an individual calibration method was proposed, but the calibration procedure increases the burden on the user. In this study, we introduce a new 3D convolutional neural network (3D CNN), which deals with spatial and temporal dimensional directions. This 3D CNN model is trained with a moving image dataset of the periocular area. For the proposed 3D CNN, a sequence of seven images cut from a video sequence is used as a set of an input sample to classify three states; voluntary, involuntary, and not blinking. In this study, data of five subjects were used for training and seven for testing. A detailed analysis of the result revealed that the biased position of the open-eye area in the images leads to a lower classification rate. To address this problem, we propose an automatic determination method for the area to be cropped in the periocular image and verify its performance. © 2023 The Institute of Electrical Engineers of Japan.