Occlusion-aware facial expression recognition: A deep learning approach

Naveen, Palanichamy

doi:10.1007/s11042-023-17013-1

Cited by 6 publications

(3 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This approach, which involves freezing feature extraction layers while retraining classification layers, is particularly useful when dealing with limited or specialized data, allowing for more accurate and tailored models. Several authors [ 85 , 94 , 100 , 107 , 110 , 127 , 129 ] adopt a fine-tuning approach, freezing feature extraction layers and retraining classification layers. Kousalya et al [ 97 ] explore optimizers, achieving training and evaluation accuracies of 87.59% and 83.59% using Stochastic Gradient Descent (SGD) optimizer.…”

Section: Resultsmentioning

confidence: 99%

Systematic Review of Emotion Detection with Computer Vision and Deep Learning

Pereira,

Mendes,

Ribeiro

et al. 2024

Sensors

View full text Add to dashboard Cite

Emotion recognition has become increasingly important in the field of Deep Learning (DL) and computer vision due to its broad applicability by using human–computer interaction (HCI) in areas such as psychology, healthcare, and entertainment. In this paper, we conduct a systematic review of facial and pose emotion recognition using DL and computer vision, analyzing and evaluating 77 papers from different sources under Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our review covers several topics, including the scope and purpose of the studies, the methods employed, and the used datasets. The scope of this work is to conduct a systematic review of facial and pose emotion recognition using DL methods and computer vision. The studies were categorized based on a proposed taxonomy that describes the type of expressions used for emotion detection, the testing environment, the currently relevant DL methods, and the datasets used. The taxonomy of methods in our review includes Convolutional Neural Network (CNN), Faster Region-based Convolutional Neural Network (R-CNN), Vision Transformer (ViT), and “Other NNs”, which are the most commonly used models in the analyzed studies, indicating their trendiness in the field. Hybrid and augmented models are not explicitly categorized within this taxonomy, but they are still important to the field. This review offers an understanding of state-of-the-art computer vision algorithms and datasets for emotion recognition through facial expressions and body poses, allowing researchers to understand its fundamental components and trends.

show abstract

Section: Resultsmentioning

confidence: 99%

Systematic Review of Emotion Detection with Computer Vision and Deep Learning

Pereira,

Mendes,

Ribeiro

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Xiao et al [31] adapted to a complex scene by constraining a joint multitasking network to assign global and local information weights. Naveen et al [32] coped with the problem of occlusion by using Lanczos interpolation to maintain image quality and combined Hopfield and DBN networks for facial expression recognition tasks. In addition to this, several researchers have used neural network search [33] for micro-expression recognition.…”

Section: Visible Light Facial Expression Recognitionmentioning

confidence: 99%

“…SL+SSLpuzzling [51] 2021 98.23 FER_RN [49] 2022 96.97 CFNet [31] 2023 99.07 DBN [32] 2023 98.19 CNN_LSTM [52] 2023 92.00 ZFER [50] 2023 98.74 CoT_AdaptiveViT(Ours) 2024 99.20…”

Section: Year Acc (%)mentioning

confidence: 99%

Context Transformer and Adaptive Method with Visual Transformer for Robust Facial Expression Recognition

Xiong,

Zhang,

Zheng

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

In real-world scenarios, the facial expression recognition task faces several challenges, including lighting variations, image noise, face occlusion, and other factors, which limit the performance of existing models in dealing with complex situations. To cope with these problems, we introduce the CoT module between the CNN and ViT frameworks, which improves the ability to perceive subtle differences by learning the correlations between local area features at a fine-grained level, helping to maintain the consistency between the local area features and the global expression, and making the model more adaptable to complex lighting conditions. Meanwhile, we adopt an adaptive learning method to effectively eliminate the interference of noise and occlusion by dynamically adjusting the parameters of the Transformer Encoder’s self-attention weight matrix. Experiments demonstrate the accuracy of our CoT_AdaViT model in the Oulu-CASIA dataset as (NIR: 87.94%, VL: strong: 89.47%, weak: 84.76%, dark: 82.28%). As well as, CK+, RAF-DB, and FERPlus datasets achieved 99.20%, 91.07%, and 90.57% recognition results, which achieved excellent performance and verified that the model has strong recognition accuracy and robustness in complex scenes.

show abstract

Improving facial expression recognition for autism with IDenseNet‐RCAformer under occlusions

Selvi,

Parvathy

2024

Intl J of Devlp Neuroscience

View full text Add to dashboard Cite

The term ‘autism spectrum disorder’ describes a neurodevelopmental illness typified by verbal and nonverbal interaction impairments, repetitive behaviour patterns and poor social interaction. Understanding mental states from FEs is crucial for interpersonal interaction and social interaction. But when there are occlusions like glasses, facial hair or self‐occlusion, it becomes harder to identify facial expressions accurately. This research tackles the issue of identifying facial expressions when parts of the face are occluded and suggests an innovative technique to tackle this difficulty. Creating a strong framework for facial expression recognition (FER) that better handles occlusions and increases recognition accuracy is the goal of this research. Therefore, we propose novel Improved DenseNet‐based Residual Cross‐Attention Transformer (IDenseNet‐RCAformer) system to tackle the partial occlusion FER problem in autism patients. The recognition framework's efficacy is assessed using four datasets of facial expressions, and some preprocessing procedures are conducted to increase the expression recognition efficiency. After that, when recognizing expressions, a simple argmax function is applied to get a forecasted landmark position from a heatmap. Then feature extraction phase, local and global representation are captured from preprocessed images by adopting Inception‐ResNet‐V2 approach, Cross‐Attention Transformer, respectively. Moreover, both features are fused by employing the FusionNet method, thereby enhancing system's training speed and precision. After the features are extracted, an improved DenseNet mechanism is applied to efficiently recognize some variety of facial expressions in partially occluded autism patients. A number of performance metrics are determined and analysed to demonstrate the proposed approach's effectiveness, where the IDenseNet‐RCAformer performs best with an accuracy of 98.95%. According to the experimental findings, the proposed framework significantly outperforms the prior recognition frameworks in terms of recognition outcomes.

show abstract

Occlusion-aware facial expression recognition: A deep learning approach

Cited by 6 publications

References 29 publications

Systematic Review of Emotion Detection with Computer Vision and Deep Learning

Systematic Review of Emotion Detection with Computer Vision and Deep Learning

Context Transformer and Adaptive Method with Visual Transformer for Robust Facial Expression Recognition

Improving facial expression recognition for autism with IDenseNet‐RCAformer under occlusions

Contact Info

Product

Resources

About