Facial Expression Recognition using Facial Landmark Detection and Feature Extraction via Neural Networks

Khan, Fuzail

doi:10.48550/arxiv.1812.04510

Cited by 8 publications

(6 citation statements)

References 13 publications

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“…Hassani et al [14] proposed a 3D Inception-ResNet where facial landmarks are multiplied with image features at certain layers. Khan [17] used the facial landmarks to crop small regions first, then generate features as the input for the neural networks. However, existing methods that utilize facial landmarks ignore the correlations of landmark features and image features.…”

Section: Related Workmentioning

confidence: 99%

POSTER: A Pyramid Cross-Fusion Transformer Network for Facial Expression Recognition

Zheng¹,

Mendieta²,

Chen³

2022

Preprint

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Facial Expression Recognition (FER) has received increasing interest in the computer vision community. As a challenging task, there are three key issues especially prevalent in FER: inter-class similarity, intra-class discrepancy, and scale sensitivity. Existing methods typically address some of these issues, but do not tackle them all in a unified framework. Therefore, in this paper, we propose a two-stream Pyramid crOss-fuSion TransformER network (POSTER) that aims to holistically solve these issues. Specifically, we design a transformer-based cross-fusion paradigm that enables effective collaboration of facial landmark and direct image features to maximize proper attention to salient facial regions. Furthermore, POSTER employs a pyramid structure to promote scale invariance. Extensive experimental results demonstrate that our POSTER outperforms SOTA methods on RAF-DB with 92.05 %, FERPlus with 91.62 %, AffectNet (7 cls) with 67.31 %, and Affect-Net (8 cls) with 63.34 %, respectively. Code is available at https: //github.com/zczcwh/POSTER

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Section: Related Workmentioning

confidence: 99%

POSTER: A Pyramid Cross-Fusion Transformer Network for Facial Expression Recognition

Zheng¹,

Mendieta²,

Chen³

2022

Preprint

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“…The facial-landmark detection from video recordings is a field of research with numerous clinically-oriented applications ranging from human expressions recognition [29,30] to fatigue detection, [31] and facial-palsy rating [32].…”

Section: Facial Landmark Detection: From Methods To the Challenges Of...mentioning

confidence: 99%

A store-and-forward cloud-based telemonitoring system for automatic assessing dysarthria evolution in neurological diseases from video-recording analysis

Migliorelli,

Berardini,

Cela

et al. 2023

Computers in Biology and Medicine

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“…Salah et al [33] conducted FER by computing the Euclidean distances among facial feature points and employing a one-dimensional deep classifier. Khan et al [35] obtained feature vectors by calculating facial landmarks, which were subsequently utilized as inputs for a neural network so as to produce the final output corresponding of facial expression categories. However, till now, few study has focused on the relationship between facial landmarks features and facial image features.…”

Section: Facial Landmarks In Fermentioning

confidence: 99%

Facial Expression Recognition with Enhanced Relation-Aware Attention and Cross-Feature Fusion transformer

DONG,

Wang,

et al. 2024

Preprint

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Face expression recognition(FER) is an important research branch in the field of the computer vision neighborhood. Three prevalent problems in FER tasks that severely impact recognition rates are inter-class similarity, intra-class differences, and facial occlusion issues. Although there have been studies that address some of these issues, none of them can adequately address all three issues in a unified framework. In this paper, we propose a novel dual-branch structure of enhanced relation-aware attention and cross-feature fusion transformer network to comprehensively solve all three issues. Specifically, we design the Enhanced Relation-Aware Attention module to maximize the exploration of more local expression features. At the same time, the Transformer Perceptual Encoder module is adopted to establishing the contextual relationship between individual patches under global information. This greatly alleviates the inter-class similarity problem and the facial occlusion and facial pose transformation problems. On the basis of a dual branch structure, we extract facial image features using facial landmarks features to guide them and design Cross-Feature Fusion Transformer module to deeply cross-fuse two different semantic features. Experiments are performed and results show that our method can greatly alleviated intra-class difference problem with comparison of several traditional methods on three commonly used datasets.

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Facial Expression Recognition using Facial Landmark Detection and Feature Extraction via Neural Networks

Cited by 8 publications

References 13 publications

POSTER: A Pyramid Cross-Fusion Transformer Network for Facial Expression Recognition

POSTER: A Pyramid Cross-Fusion Transformer Network for Facial Expression Recognition

A store-and-forward cloud-based telemonitoring system for automatic assessing dysarthria evolution in neurological diseases from video-recording analysis

Facial Expression Recognition with Enhanced Relation-Aware Attention and Cross-Feature Fusion transformer

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