Blink Detection for Off-Angle Iris Recognition Using Deep Learning

Iris biometric systems offer non-contact authentication, particularly advantageous in controlled environments such as security checkpoints. However, challenges arise in less controlled scenarios such as standoff biometrics where captured images mostly are non-ideal including off-angle. This paper addresses the need for iris recognition models adaptable to various gaze angles by proposing a blink detection algorithm as an additional feature. The study explores different blink detection methods including involving logistic regression, random forest, and deep learning models. For the first methodology, logistic regression and a random forest model were used to classify eye images into four different blink classes. The second methodology involved labeling eye openness percentage. The ground-truth eye blink was calculated using facial landmarks detected by the MediaPipe model. For the deep learning approach, we used a pre-trained Convolutional Neural Network (CNN) model by replacing the output layer with a regression layer. Results show improved precision and recall when incorporating height and width features for the regression model. The AlexNet model achieves superior performance, reaching 90% accuracy with a 10
^%
error threshold. This research contributes valuable insights for developing robust iris recognition models adaptable to diverse gaze angles.