1,840 research outputs found
Facial expression recognition in the wild : from individual to group
The progress in computing technology has increased the demand for smart systems capable of understanding human affect and emotional manifestations. One of the crucial factors in designing systems equipped with such intelligence is to have accurate automatic Facial Expression Recognition (FER) methods. In computer vision, automatic facial expression analysis is an active field of research for over two decades now. However, there are still a lot of questions unanswered. The research presented in this thesis attempts to address some of the key issues of FER in challenging conditions mentioned as follows: 1) creating a facial expressions database representing real-world conditions; 2) devising Head Pose Normalisation (HPN) methods which are independent of facial parts location; 3) creating automatic methods for the analysis of mood of group of people. The central hypothesis of the thesis is that extracting close to real-world data from movies and performing facial expression analysis on movies is a stepping stone in the direction of moving the analysis of faces towards real-world, unconstrained condition. A temporal facial expressions database, Acted Facial Expressions in the Wild (AFEW) is proposed. The database is constructed and labelled using a semi-automatic process based on closed caption subtitle based keyword search. Currently, AFEW is the largest facial expressions database representing challenging conditions available to the research community. For providing a common platform to researchers in order to evaluate and extend their state-of-the-art FER methods, the first Emotion Recognition in the Wild (EmotiW) challenge based on AFEW is proposed. An image-only based facial expressions database Static Facial Expressions In The Wild (SFEW) extracted from AFEW is proposed. Furthermore, the thesis focuses on HPN for real-world images. Earlier methods were based on fiducial points. However, as fiducial points detection is an open problem for real-world images, HPN can be error-prone. A HPN method based on response maps generated from part-detectors is proposed. The proposed shape-constrained method does not require fiducial points and head pose information, which makes it suitable for real-world images. Data from movies and the internet, representing real-world conditions poses another major challenge of the presence of multiple subjects to the research community. This defines another focus of this thesis where a novel approach for modeling the perception of mood of a group of people in an image is presented. A new database is constructed from Flickr based on keywords related to social events. Three models are proposed: averaging based Group Expression Model (GEM), Weighted Group Expression Model (GEM_w) and Augmented Group Expression Model (GEM_LDA). GEM_w is based on social contextual attributes, which are used as weights on each person's contribution towards the overall group's mood. Further, GEM_LDA is based on topic model and feature augmentation. The proposed framework is applied to applications of group candid shot selection and event summarisation. The application of Structural SIMilarity (SSIM) index metric is explored for finding similar facial expressions. The proposed framework is applied to the problem of creating image albums based on facial expressions, finding corresponding expressions for training facial performance transfer algorithms
Facial Expression Analysis under Partial Occlusion: A Survey
Automatic machine-based Facial Expression Analysis (FEA) has made substantial
progress in the past few decades driven by its importance for applications in
psychology, security, health, entertainment and human computer interaction. The
vast majority of completed FEA studies are based on non-occluded faces
collected in a controlled laboratory environment. Automatic expression
recognition tolerant to partial occlusion remains less understood, particularly
in real-world scenarios. In recent years, efforts investigating techniques to
handle partial occlusion for FEA have seen an increase. The context is right
for a comprehensive perspective of these developments and the state of the art
from this perspective. This survey provides such a comprehensive review of
recent advances in dataset creation, algorithm development, and investigations
of the effects of occlusion critical for robust performance in FEA systems. It
outlines existing challenges in overcoming partial occlusion and discusses
possible opportunities in advancing the technology. To the best of our
knowledge, it is the first FEA survey dedicated to occlusion and aimed at
promoting better informed and benchmarked future work.Comment: Authors pre-print of the article accepted for publication in ACM
Computing Surveys (accepted on 02-Nov-2017
Towards Realistic Facial Expression Recognition
Automatic facial expression recognition has attracted significant attention over the past decades. Although substantial progress has been achieved for certain scenarios (such as frontal faces in strictly controlled laboratory settings), accurate recognition of facial expression in realistic environments remains unsolved for the most part. The main objective of this thesis is to investigate facial expression recognition in unconstrained environments. As one major problem faced by the literature is the lack of realistic training and testing data, this thesis presents a web search based framework to collect realistic facial expression dataset from the Web. By adopting an active learning based method to remove noisy images from text based image search results, the proposed approach minimizes the human efforts during the dataset construction and maximizes the scalability for future research. Various novel facial expression features are then proposed to address the challenges imposed by the newly collected dataset. Finally, a spectral embedding based feature fusion framework is presented to combine the proposed facial expression features to form a more descriptive representation. This thesis also systematically investigates how the number of frames of a facial expression sequence can affect the performance of facial expression recognition algorithms, since facial expression sequences may be captured under different frame rates in realistic scenarios. A facial expression keyframe selection method is proposed based on keypoint based frame representation. Comprehensive experiments have been performed to demonstrate the effectiveness of the presented methods
3d Face Reconstruction And Emotion Analytics With Part-Based Morphable Models
3D face reconstruction and facial expression analytics using 3D facial data are new
and hot research topics in computer graphics and computer vision. In this proposal, we first
review the background knowledge for emotion analytics using 3D morphable face model, including
geometry feature-based methods, statistic model-based methods and more advanced
deep learning-bade methods. Then, we introduce a novel 3D face modeling and reconstruction
solution that robustly and accurately acquires 3D face models from a couple of images
captured by a single smartphone camera. Two selfie photos of a subject taken from the
front and side are used to guide our Non-Negative Matrix Factorization (NMF) induced
part-based face model to iteratively reconstruct an initial 3D face of the subject. Then, an
iterative detail updating method is applied to the initial generated 3D face to reconstruct
facial details through optimizing lighting parameters and local depths. Our iterative 3D
face reconstruction method permits fully automatic registration of a part-based face representation
to the acquired face data and the detailed 2D/3D features to build a high-quality
3D face model. The NMF part-based face representation learned from a 3D face database
facilitates effective global and adaptive local detail data fitting alternatively. Our system
is flexible and it allows users to conduct the capture in any uncontrolled environment. We
demonstrate the capability of our method by allowing users to capture and reconstruct their
3D faces by themselves.
Based on the 3D face model reconstruction, we can analyze the facial expression and
the related emotion in 3D space. We present a novel approach to analyze the facial expressions
from images and a quantitative information visualization scheme for exploring this
type of visual data. From the reconstructed result using NMF part-based morphable 3D face
model, basis parameters and a displacement map are extracted as features for facial emotion
analysis and visualization. Based upon the features, two Support Vector Regressions (SVRs)
are trained to determine the fuzzy Valence-Arousal (VA) values to quantify the emotions.
The continuously changing emotion status can be intuitively analyzed by visualizing the
VA values in VA-space. Our emotion analysis and visualization system, based on 3D NMF
morphable face model, detects expressions robustly from various head poses, face sizes and
lighting conditions, and is fully automatic to compute the VA values from images or a sequence
of video with various facial expressions. To evaluate our novel method, we test our
system on publicly available databases and evaluate the emotion analysis and visualization
results. We also apply our method to quantifying emotion changes during motivational interviews.
These experiments and applications demonstrate effectiveness and accuracy of
our method.
In order to improve the expression recognition accuracy, we present a facial expression
recognition approach with 3D Mesh Convolutional Neural Network (3DMCNN) and a visual
analytics guided 3DMCNN design and optimization scheme. The geometric properties of the
surface is computed using the 3D face model of a subject with facial expressions. Instead of
using regular Convolutional Neural Network (CNN) to learn intensities of the facial images,
we convolve the geometric properties on the surface of the 3D model using 3DMCNN. We
design a geodesic distance-based convolution method to overcome the difficulties raised from
the irregular sampling of the face surface mesh. We further present an interactive visual
analytics for the purpose of designing and modifying the networks to analyze the learned
features and cluster similar nodes in 3DMCNN. By removing low activity nodes in the network,
the performance of the network is greatly improved. We compare our method with the regular CNN-based method by interactively visualizing each layer of the networks and
analyze the effectiveness of our method by studying representative cases. Testing on public
datasets, our method achieves a higher recognition accuracy than traditional image-based
CNN and other 3D CNNs. The presented framework, including 3DMCNN and interactive
visual analytics of the CNN, can be extended to other applications
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