MR-CapsNet: A Deep Learning Algorithm for Image-Based Head Pose Estimation on CapsNet

Abstract

Head pose estimation based on a single image is a challenging endeavor because of the complex background conditions and characteristics of the human face. In this report, we propose a Multi stage Regression-Capsule Network (MR-CapsNet) to predict head posture based on a single image input. In the study, we used the residual attention block and squeeze-and-excitation block to extract features in three levels. CapsNet overcomes the shortcomings of the traditional convolutional neural network and implements module aggregation to describe the spatial relationship of features after aggregation, in addition to realizing a compact and robust model using a multi-stage regression scheme. We tested our method on the AFLW2000 and BIWI datasets obtaining mean absolute errors of 4.26% and 3.95%, respectively. In addition, we discuss the accuracy of our method in the case of eye or mouth occlusion. The results of comprehensive experiments reveal that our method can accurately predict head posture.

Modified Salp Swarm Algorithm With Deep Learning Based Gastrointestinal Tract Disease Classification on Endoscopic Images

“…In the presented model, the improved CapsNet model is used for feature extraction. CapsNet architecture is established to maintain the location of an object and its features in image and model hierarchical relationship [28]. In the CNN model, valuable information arrives before the pooling layer.…”