Face-changing microfilm State Grid

Can facial Grid Transformation improve feature extraction for recognizing facial action units?

In this work, we propose a novel face registration method named facial grid transformation to improve feature extraction for recognizing facial Action Units (AUs). Based on the transformed grid, novel grid edge features are developed to capture local facial motions related to AUs.

How do we initialize the network weights of MS-celeb-1m face recognition dataset?

We initialize the whole network weights by the pre-trained weights on MS-Celeb-1M face recognition dataset. Without loss of generalization, the ASF is utilized to combine the features extracted from the RGB image and the features extracted from its LBP feature image, which will be introduced in Section 3.2 in detail.

Do CNN-based fer models capture only facial features?

Therefore, CNN-based FER models capture only facial features, without understanding facial expression images globally. There are two categories of solutions to mitigate this problem in the literature. The first category is to increase kernel sizes , increase model depth , or adopt new operations like global pooling .

Can CNNs regress 3DMM PA- meters from a single image?

More recently, 3DMMs and differentiable ren- derershavebeenusedasmodel-baseddecoderstotraincon- volutional neural networks (CNNs) to regress 3DMM pa- rameters directly from a single image [29].

Which wafer-scale conductive MOF films can be produced?

Other wafer-scale conductive MOF films such as M 3 (HHTP) 2 (M = Cu, Co, and Ni; HHTP = 2,3,6,7,10,11-triphenylenehexol) can be produced utilizing this strategy and suggests this method has widescale applicability potential. The authors declare no conflict of interest.