ﻻ يوجد ملخص باللغة العربية
Automatic pain intensity assessment has a high value in disease diagnosis applications. Inspired by the fact that many diseases and brain disorders can interrupt normal facial expression formation, we aim to develop a computational model for automatic pain intensity assessment from spontaneous and micro facial variations. For this purpose, we propose a 3D deep architecture for dynamic facial video representation. The proposed model is built by stacking several convolutional modules where each module encompasses a 3D convolution kernel with a fixed temporal depth, several parallel 3D convolutional kernels with different temporal depths, and an average pooling layer. Deploying variable temporal depths in the proposed architecture allows the model to effectively capture a wide range of spatiotemporal variations on the faces. Extensive experiments on the UNBC-McMaster Shoulder Pain Expression Archive database show that our proposed model yields in a promising performance compared to the state-of-the-art in automatic pain intensity estimation.
Automatic estimation of pain intensity from facial expressions in videos has an immense potential in health care applications. However, domain adaptation (DA) is needed to alleviate the problem of domain shifts that typically occurs between video dat
Estimation of pain intensity from facial expressions captured in videos has an immense potential for health care applications. Given the challenges related to subjective variations of facial expressions, and operational capture conditions, the accura
Face representation is a crucial step of face recognition systems. An optimal face representation should be discriminative, robust, compact, and very easy-to-implement. While numerous hand-crafted and learning-based representations have been proposed
We propose an automatic method for pain intensity measurement from video. For each video, pain intensity was measured using the dynamics of facial movement using 66 facial points. Gram matrices formulation was used for facial points trajectory repres
Managing post-surgical pain is critical for successful surgical outcomes. One of the challenges of pain management is accurately assessing the pain level of patients. Self-reported numeric pain ratings are limited because they are subjective, can be