Face Trees for Expression Recognition
- URL: http://arxiv.org/abs/2112.02487v1
- Date: Sun, 5 Dec 2021 06:35:12 GMT
- Title: Face Trees for Expression Recognition
- Authors: Mojtaba Kolahdouzi, Alireza Sepas-Moghaddam, Ali Etemad
- Abstract summary: We propose an end-to-end architecture for facial expression recognition.
The proposed architecture incorporates two main streams, one focusing on landmark positions to learn the structure of the face, the other focuses on patches around the landmarks to learn texture information.
We conduct extensive experiments on two large-scale publicly available facial expression datasets, AffectNet and FER2013, to evaluate the efficacy of our approach.
- Score: 13.099925083569333
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: We propose an end-to-end architecture for facial expression recognition. Our
model learns an optimal tree topology for facial landmarks, whose traversal
generates a sequence from which we obtain an embedding to feed a sequential
learner. The proposed architecture incorporates two main streams, one focusing
on landmark positions to learn the structure of the face, while the other
focuses on patches around the landmarks to learn texture information. Each
stream is followed by an attention mechanism and the outputs are fed to a
two-stream fusion component to perform the final classification. We conduct
extensive experiments on two large-scale publicly available facial expression
datasets, AffectNet and FER2013, to evaluate the efficacy of our approach. Our
method outperforms other solutions in the area and sets new state-of-the-art
expression recognition rates on these datasets.
Related papers
- Towards Multi-domain Face Landmark Detection with Synthetic Data from
Diffusion model [27.307563102526192]
deep learning-based facial landmark detection for in-the-wild faces has achieved significant improvement.
There are still challenges in face landmark detection in other domains (e.g. cartoon, caricature, etc)
We design a two-stage training approach that effectively leverages limited datasets and the pre-trained diffusion model.
Our results demonstrate that our method outperforms existing methods on multi-domain face landmark detection.
arXiv Detail & Related papers (2024-01-24T02:35:32Z) - FaceTopoNet: Facial Expression Recognition using Face Topology Learning [23.139108533273777]
We propose an end-to-end deep model for facial expression recognition, which is capable of learning an effective tree topology of the face.
Our model then traverses the learned tree to generate a sequence, which is then used to form an embedding to feed a sequential learner.
We perform extensive experiments on four large-scale in-the-wild facial expression datasets to evaluate our approach.
arXiv Detail & Related papers (2022-09-13T22:02:54Z) - Learning Semantics for Visual Place Recognition through Multi-Scale
Attention [14.738954189759156]
We present the first VPR algorithm that learns robust global embeddings from both visual appearance and semantic content of the data.
Experiments on various scenarios validate this new approach and demonstrate its performance against state-of-the-art methods.
arXiv Detail & Related papers (2022-01-24T14:13:12Z) - A Unified Architecture of Semantic Segmentation and Hierarchical
Generative Adversarial Networks for Expression Manipulation [52.911307452212256]
We develop a unified architecture of semantic segmentation and hierarchical GANs.
A unique advantage of our framework is that on forward pass the semantic segmentation network conditions the generative model.
We evaluate our method on two challenging facial expression translation benchmarks, AffectNet and RaFD, and a semantic segmentation benchmark, CelebAMask-HQ.
arXiv Detail & Related papers (2021-12-08T22:06:31Z) - Progressive Spatio-Temporal Bilinear Network with Monte Carlo Dropout
for Landmark-based Facial Expression Recognition with Uncertainty Estimation [93.73198973454944]
The performance of our method is evaluated on three widely used datasets.
It is comparable to that of video-based state-of-the-art methods while it has much less complexity.
arXiv Detail & Related papers (2021-06-08T13:40:30Z) - Deep Structured Prediction for Facial Landmark Detection [59.60946775628646]
This paper proposes a method for deep structured facial landmark detection based on combining a deep Convolutional Network with a Conditional Random Field.
We demonstrate its superior performance to existing state-of-the-art techniques in facial landmark detection.
arXiv Detail & Related papers (2020-10-18T17:09:24Z) - The Elements of End-to-end Deep Face Recognition: A Survey of Recent
Advances [56.432660252331495]
Face recognition is one of the most popular and long-standing topics in computer vision.
Deep face recognition has made remarkable progress and been widely used in many real-world applications.
In this survey article, we present a comprehensive review about the recent advance of each element.
arXiv Detail & Related papers (2020-09-28T13:02:17Z) - Unsupervised Deep Cross-modality Spectral Hashing [65.3842441716661]
The framework is a two-step hashing approach which decouples the optimization into binary optimization and hashing function learning.
We propose a novel spectral embedding-based algorithm to simultaneously learn single-modality and binary cross-modality representations.
We leverage the powerful CNN for images and propose a CNN-based deep architecture to learn text modality.
arXiv Detail & Related papers (2020-08-01T09:20:11Z) - Structured Landmark Detection via Topology-Adapting Deep Graph Learning [75.20602712947016]
We present a new topology-adapting deep graph learning approach for accurate anatomical facial and medical landmark detection.
The proposed method constructs graph signals leveraging both local image features and global shape features.
Experiments are conducted on three public facial image datasets (WFLW, 300W, and COFW-68) as well as three real-world X-ray medical datasets (Cephalometric (public), Hand and Pelvis)
arXiv Detail & Related papers (2020-04-17T11:55:03Z) - Deep Multi-Facial Patches Aggregation Network For Facial Expression
Recognition [5.735035463793008]
We propose an approach for Facial Expressions Recognition (FER) based on a deep multi-facial patches aggregation network.
Deep features are learned from facial patches using deep sub-networks and aggregated within one deep architecture for expression classification.
arXiv Detail & Related papers (2020-02-20T17:57:06Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.