MobiFace: A Novel Dataset for Mobile Face Tracking in the Wild

Face tracking serves as the crucial initial step in mobile applications trying to analyse target faces over time in mobile settings. However, this problem has received little attention, mainly due to the scarcity of dedicated face tracking benchmarks. In this work, we introduce MobiFace, the first dataset for single face tracking in mobile situations. It consists of 80 unedited live-streaming mobile videos captured by 70 different smartphone users in fully unconstrained environments. Over $95K$ bounding boxes are manually labelled. The videos are carefully selected to cover typical smartphone usage. The videos are also annotated with 14 attributes, including 6 newly proposed attributes and 8 commonly seen in object tracking. 36 state-of-the-art trackers, including facial landmark trackers, generic object trackers and trackers that we have fine-tuned or improved, are evaluated. The results suggest that mobile face tracking cannot be solved through existing approaches. In addition, we show that fine-tuning on the MobiFace training data significantly boosts the performance of deep learning-based trackers, suggesting that MobiFace captures the unique characteristics of mobile face tracking. Our goal is to offer the community a diverse dataset to enable the design and evaluation of mobile face trackers. The dataset, annotations and the evaluation server will be on \url{https://mobiface.github.io/}.Comment: To appear on The 14th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2019

Two-phase nanofluid over rotating disk with exponential variable thickness

Purpose: The purpose of this paper is to investigate the effect of nanofluid over rotating disk with the exponential variable thickness (Formula presented.) (c &gt; 0, b &gt; 0) and to analyze Brownian motion and thermophoresis of Buongiorno model on the disk embedded in nanofluid-saturated porous media.Design/methodology/approach: Using the generalized von Karman transformation, the boundary layer governing equations are transformed into semi-similar forms solved by bvp4c in MATLAB.Findings: The effects of the thickness parameter a, the shape parameter b, the Brownian motion parameter Nb and thermophoresis parameter Nt on flow, heat and mass transfer are analyzed. With the increase of thickness parameter a, the radial velocity first decreases and then increases, showing the opposite trend on the two sides of the peak value. Moreover, temperature and concentration rise as the Brownian motion parameter Nb becomes larger.Originality/value: To the best of the authors’ knowledge, this is the first work that has been done on rotating disk with exponential variable thickness in nanofluid. The impact of the two slip effects, namely, Brownian motion and thermophoresis, on the nanofluid boundary layer flow, heat and mass transfer because of rotating disk with exponential variable thickness (Formula presented.) (c &gt; 0, b &gt; 0) has been addressed in this study.</p

A novel buckwheat protein with a beneficial effect in atherosclerosis was purified from Fagopyrum tataricum (L.) Gaertn.

Buckwheat seeds contain many kinds of functional compounds that are of benefit to patients with cardiovascular disease. In this research, a water-soluble buckwheat protein was isolated and purified through a DEAE-Sepharose anion exchange column and Sephadex G-75 gel chromatography. The isolated buckwheat protein fractions exhibited hypocholesterolemic activity in a HepG2 cell model and demonstrated prominent bile acid salt-binding activity in an in vitro assay. The antioxidative activity of protein fractions with hypolipidemic effects was detected in a free radical scavenging experiment. The buckwheat protein fraction with the most obvious hypolipidemic activity and free radical scavenging activity was named as WSBWP. Its molecular weight was estimated by SDS-PAGE electrophoresis to be 38 kDa. It could become a potential candidate in the treatment of atherosclerosis

Deep face tracking and parsing in the wild

Face analysis has been a long-standing research direction in the field of computer vision and pattern recognition. A complete face analysis system involves solving several tasks including face detection, face tracking, face parsing, and face recognition. Recently, the performance of methods in all tasks has significantly improved thanks to the employment of Deep Convolutional Neural Networks (DCNNs). However, existing face analysis algorithms mainly focus on solving facial images captured in the constrained laboratory environment, and their performance on real-world images has remained less explored. Compared with the lab environment, the in-the-wild settings involve greater diversity in face sizes, poses, facial expressions, background clutters, lighting conditions and imaging quality. This thesis investigates two fundamental tasks in face analysis under in-the-wild settings: face tracking and face parsing. Both tasks serve as important prerequisites for downstream face analysis applications. However, in-the-wild datasets remain scarce in both fields and models have not been rigorously evaluated in such settings. In this thesis, we aim to bridge that gap of lacking in-the-wild data, evaluate existing methods in these settings, and develop accurate, robust and efficient deep learning-based methods for the two tasks. For face tracking in the wild, we introduce the first in-the-wild face tracking dataset, MobiFace, that consists of 80 videos captured by mobile phones during mobile live-streaming. The environment of the live-streaming performance is fully unconstrained and the interactions between users and mobile phones are natural and spontaneous. Next, we evaluate existing tracking methods, including generic object trackers and dedicated face trackers. The results show that MobiFace represent unique challenges in face tracking in the wild and cannot be readily solved by existing methods. Finally, we present a DCNN-based framework, FT-RCNN, that significantly outperforms other methods in face tracking in the wild. For face parsing in the wild, we introduce the first large-scale in-the-wild face dataset, iBugMask, that contains 21, 866 training images and 1, 000 testing images. Unlike existing datasets, the images in iBugMask are captured in the fully unconstrained environment and are not cropped or preprocessed of any kind. Manually annotated per-pixel labels for eleven facial regions are provided for each target face. Next, we benchmark existing parsing methods and the results show that iBugMask is extremely challenging for all methods. By rigorous benchmarking, we observe that the pre-processing of facial images with bounding boxes in face parsing in the wild introduces bias. When cropping the face with a bounding box, a cropping margin has to be hand-picked. If face alignment is used, fiducial landmarks are required and a predefined alignment template has to be selected. These additional hyper-parameters have to be carefully considered and can have a significant impact on the face parsing performance. To solve this, we propose Region-of-Interest (RoI) Tanh-polar transform that warps the whole image to a fixed-sized representation. Moreover, the RoI Tanh-polar transform is differentiable and allows for rotation equivariance in 1 DCNNs. We show that when coupled with a simple Fully Convolutional Network, our RoI Tanh-polar transformer Network has achieved state-of-the-art results on face parsing in the wild. This thesis contributes towards in-the-wild face tracking and face parsing by providing novel datasets and proposing effective frameworks. Both tasks can benefit real-world downstream applications such as facial age estimation, facial expression recognition and lip-reading. The proposed RoI Tanh-polar transform also provides a new perspective in how to preprocess the face images and make the DCNNs truly end-to-end for real-world face analysis applications.Open Acces