Compensating inaccurate annotations to train 3D facial landmark localisation models

In this paper we investigate the impact of inconsistency in manual annotations when they are used to train automatic models for 3D facial landmark localization. We start by showing that it is possible to objectively measure the consistency of annotations in a database, provided that it contains replicates (i.e. repeated scans from the same person). Applying such measure to the widely used FRGC database we find that manual annotations currently available are suboptimal and can strongly impair the accuracy of automatic models learnt therefrom. To address this issue, we present a simple algorithm to automatically correct a set of annotations and show that it can help to significantly improve the accuracy of the models in terms of landmark localization errors. This improvement is observed even when errors are measured with respect to the original (not corrected) annotations. However, we also show that if errors are computed against an alternative set of manual annotations with higher consistency, the accuracy of the models constructed using the corrections from the presented algorithm tends to converge to the one achieved by building the models on the alternative,more consistent set

Beauty3DFaceNet:Deep geometry and texture fusion for 3D facial attractiveness prediction

We present Beauty3DFaceNet, the first deep convolutional neural network to predict attractiveness on 3D faces with both geometry and texture information. The proposed network can learn discriminative and complementary 2D and 3D facial features, allowing accurate attractiveness prediction for 3D faces. The main component of our network is a fusion module that fuses geometric features and texture features. We further employ a novel sampling strategy for our network based on a prior of facial landmarks, which improves the performance of learning aesthetic features from a face point cloud. Comparing to previous work, our approach takes full advantage of 3D geometry and 2D texture and does not rely on handcrafted features based on highly accurate facial characteristics such as feature points. To facilitate 3D facial attractiveness research, we also construct the first 3D face dataset ShadowFace3D, which contains 6,000 high-quality 3D faces with attractiveness labeled by human annotators. Extensive quantitative and qualitative evaluations show that Beauty3DFaceNet achieves a significant correlation with the average human ratings. This validates that a deep learning network can effectively learn and predict 3D facial attractiveness.</p

Computer analysis of face beauty: a survey

The human face conveys to other human beings, and potentially to computer systems, information such as identity, intentions, emotional and health states, attractiveness, age, gender and ethnicity. In most cases analyzing this information involves the computer science as well as the human and medical sciences. The most studied multidisciplinary problems are analyzing emotions, estimating age and modeling aging effects. An emerging area is the analysis of human attractiveness. The purpose of this paper is to survey recent research on the computer analysis of human beauty. First we present results in human sciences and medicine pointing to a largely shared and data-driven perception of attractiveness, which is a rationale of computer beauty analysis. After discussing practical application areas, we survey current studies on the automatic analysis of facial attractiveness aimed at: i) relating attractiveness to particular facial features; ii) assessing attractiveness automatically; iii) improving the attractiveness of 2D or 3D face images. Finally we discuss open problems and possible lines of research

Co-sputtered MoRe thin films for carbon nanotube growth-compatible superconducting coplanar resonators

Molybdenum rhenium alloy thin films can exhibit superconductivity up to critical temperatures of $T_c=15\mathrm{K}$. At the same time, the films are highly stable in the high-temperature methane / hydrogen atmosphere typically required to grow single wall carbon nanotubes. We characterize molybdenum rhenium alloy films deposited via simultaneous sputtering from two sources, with respect to their composition as function of sputter parameters and their electronic dc as well as GHz properties at low temperature. Specific emphasis is placed on the effect of the carbon nanotube growth conditions on the film. Superconducting coplanar waveguide resonators are defined lithographically; we demonstrate that the resonators remain functional when undergoing nanotube growth conditions, and characterize their properties as function of temperature. This paves the way for ultra-clean nanotube devices grown in situ onto superconducting coplanar waveguide circuit elements.Comment: 8 pages, 6 figure

Informing the design of a multisensory learning environment for elementary mathematics learning

It is well known that primary school children may face difficulties in acquiring mathematical competence, possibly because teaching is generally based on formal lessons with little opportunity to exploit more multisensory-based activities within the classroom. To overcome such difficulties, we report here the exemplary design of a novel multisensory learning environment for teaching mathematical concepts based on meaningful inputs from elementary school teachers. First, we developed and administered a questionnaire to 101 teachers asking them to rate based on their experience the learning difficulty for specific arithmetical and geometrical concepts encountered by elementary school children. Additionally, the questionnaire investigated the feasibility to use multisensory information to teach mathematical concepts. Results show that challenging concepts differ depending on children school level, thus providing a guidance to improve teaching strategies and the design of new and emerging learning technologies accordingly. Second, we obtained specific and practical design inputs with workshops involving elementary school teachers and children. Altogether, these findings are used to inform the design of emerging multimodal technological applications, that take advantage not only of vision but also of other sensory modalities. In the present work, we describe in detail one exemplary multisensory environment design based on the questionnaire results and design ideas from the workshops: the Space Shapes game, which exploits visual and haptic/proprioceptive sensory information to support mental rotation, 2D–3D transformation and percentages. Corroborating research evidence in neuroscience and pedagogy, our work presents a functional approach to develop novel multimodal user interfaces to improve education in the classroom