FEAFA: A Well-Annotated Dataset for Facial Expression Analysis and 3D Facial Animation

Facial expression analysis based on machine learning requires large number of well-annotated data to reflect different changes in facial motion. Publicly available datasets truly help to accelerate research in this area by providing a benchmark resource, but all of these datasets, to the best of our knowledge, are limited to rough annotations for action units, including only their absence, presence, or a five-level intensity according to the Facial Action Coding System. To meet the need for videos labeled in great detail, we present a well-annotated dataset named FEAFA for Facial Expression Analysis and 3D Facial Animation. One hundred and twenty-two participants, including children, young adults and elderly people, were recorded in real-world conditions. In addition, 99,356 frames were manually labeled using Expression Quantitative Tool developed by us to quantify 9 symmetrical FACS action units, 10 asymmetrical (unilateral) FACS action units, 2 symmetrical FACS action descriptors and 2 asymmetrical FACS action descriptors, and each action unit or action descriptor is well-annotated with a floating point number between 0 and 1. To provide a baseline for use in future research, a benchmark for the regression of action unit values based on Convolutional Neural Networks are presented. We also demonstrate the potential of our FEAFA dataset for 3D facial animation. Almost all state-of-the-art algorithms for facial animation are achieved based on 3D face reconstruction. We hence propose a novel method that drives virtual characters only based on action unit value regression of the 2D video frames of source actors.Comment: 9 pages, 7 figure

EM-Type Algorithms for DOA Estimation in Unknown Nonuniform Noise

The expectation--maximization (EM) algorithm updates all of the parameter estimates simultaneously, which is not applicable to direction of arrival (DOA) estimation in unknown nonuniform noise. In this work, we present several efficient EM-type algorithms, which update the parameter estimates sequentially, for solving both the deterministic and stochastic maximum--likelihood (ML) direction finding problems in unknown nonuniform noise. Specifically, we design a generalized EM (GEM) algorithm and a space-alternating generalized EM (SAGE) algorithm for computing the deterministic ML estimator. Simulation results show that the SAGE algorithm outperforms the GEM algorithm. Moreover, we design two SAGE algorithms for computing the stochastic ML estimator, in which the first updates the DOA estimates simultaneously while the second updates the DOA estimates sequentially. Simulation results show that the second SAGE algorithm outperforms the first one.Comment: arXiv admin note: text overlap with arXiv:2208.0751

BITS-Net: Blind Image Transparency Separation Network

This research presents a new approach for blind single-image transparency separation, a significant challenge in image processing. The proposed framework divides the task into two parallel processes: feature separation and image reconstruction. The feature separation task leverages two deep image prior (DIP) networks to recover two distinct layers. An exclusion loss and deep feature separation loss are used to decompose features. For the image reconstruction task, we minimize the difference between the mixed image and the re-mixed image while also incorporating a regularizer to impose natural priors on each layer. Our results indicate that our method performs comparably or outperforms state-of-the-art approaches when tested on various image datasets

Role of anti-DFS70 antibodies in idiopathic interstitial pneumonia and in connective tissue disease associated interstitial lung disease

Anti-DFS70 antibodies, corresponding to the dense fine speckled ANA indirect immunofluorescence pattern in HEp-2 substrates, have been observed in chronic inflammatory conditions, cancer and in healthy individuals but only in a small percentage of patients with systemic autoimmune rheumatic diseases. The aim of this study was to investigate the role of anti-DFS70 antibodies as biomarker in ILD. We evaluated its value to distinguish connective tissue disease associated interstitial lung disease (CTD-ILD) from idiopathic interstitial pneumonia (IIP). In addition, we explored potential correlations between anti-DFS70 antibodies and clinical parameters. Methodologically, serum samples were collected from 49 healthy controls, 35 scleroderma-ILD (SSc-ILD) patients as negative controls for anti-DFS70 antibody, and 260 patients with ILDs. The ILD patients included 100 nonspecific interstitial pneumonia (NSIP) and 160 idiopathic pulmonary fibrosis (IPF) patients. The serum anti-DFS70 antibodies were assessed by enzyme-linked immunosorbent assay. The frequency and levels of serum anti-DFS70 antibodies were lower in ILD and SSc-ILD patients compared to healthy controls. Thirty-seven patients developed CTD during 24 months of follow-up (3 initial IPF and 34 initial idiopathic NSIP patients), most of them combined with ANA positivity and anti-DFS70 antibody negativity. Anti-DFS70 antibodies were not significantly different between CTD-ILD and idiopathic ILD. Anti-DFS70 antibody concentrations were inversely correlated with pulmonary functions in iNSIP. In conclusion, the frequency and levels of serum anti-DFS70 antibodies are markedly decreased in patients with ILDs. Anti-DFS70 antibodies may play a role to predict CTD development in ILD patients