Facial Expression Recognition using Convolutional Neural Network on Graphs

Wu, Chenhui; Chai, Lu; Yang, Jun; Sheng, Yuxia

doi:10.23919/chicc.2019.8866311

Cited by 16 publications

(3 citation statements)

References 11 publications

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“…Wu et al [274] proposed a new method for facial expression recognition using a Graph Convolutional Network (GCN) which is capable of processing non-Euclidean structured data. The methodology entails the construction of an undirected graph from facial images, which is achieved by amalgamating both fixed and random points.…”

Section: Graphsmentioning

confidence: 99%

Advances in Facial Expression Recognition: A Survey of Methods, Benchmarks, Models, and Datasets

Kopalidis,

Solachidis,

Vretos

et al. 2024

Information

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Recent technological developments have enabled computers to identify and categorize facial expressions to determine a person’s emotional state in an image or a video. This process, called “Facial Expression Recognition (FER)”, has become one of the most popular research areas in computer vision. In recent times, deep FER systems have primarily concentrated on addressing two significant challenges: the problem of overfitting due to limited training data availability, and the presence of expression-unrelated variations, including illumination, head pose, image resolution, and identity bias. In this paper, a comprehensive survey is provided on deep FER, encompassing algorithms and datasets that offer insights into these intrinsic problems. Initially, this paper presents a detailed timeline showcasing the evolution of methods and datasets in deep facial expression recognition (FER). This timeline illustrates the progression and development of the techniques and data resources used in FER. Then, a comprehensive review of FER methods is introduced, including the basic principles of FER (components such as preprocessing, feature extraction and classification, and methods, etc.) from the pro-deep learning era (traditional methods using handcrafted features, i.e., SVM and HOG, etc.) to the deep learning era. Moreover, a brief introduction is provided related to the benchmark datasets (there are two categories: controlled environments (lab) and uncontrolled environments (in the wild)) used to evaluate different FER methods and a comparison of different FER models. Existing deep neural networks and related training strategies designed for FER, based on static images and dynamic image sequences, are discussed. The remaining challenges and corresponding opportunities in FER and the future directions for designing robust deep FER systems are also pinpointed.

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Section: Graphsmentioning

confidence: 99%

Advances in Facial Expression Recognition: A Survey of Methods, Benchmarks, Models, and Datasets

Kopalidis,

Solachidis,

Vretos

et al. 2024

Information

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show abstract

“…Orthognathic surgery has been studied for its effects on both face expression and mental health ([38]). Computer-aided facial expression categorization was given by Wu et al [39]. They employed a convolutional neural network to diagnose normal and abnormal faces.…”

Section: Literature Reviewmentioning

confidence: 99%

Mathematical Model and Artificial Intelligence for Diagnosis of Alzheimer's Disease

Davodabadi

Daneshian

Saati

et al. 2023

Preprint

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Degeneration of the neurological system linked to cognitive deficits, daily living exercise clutters, and behavioral disturbing impacts may define Alzheimer's disease. Ad research conducted later in life focuses on describing ways for early detection of dementia, a kind of mental disorder. To tailor our care to each patient, we utilized visual cues to determine how they were feeling. We did this by outlining two approaches to diagnosing a person's mental health. Support vector machine is the first technique (SVM). Image characteristics are extracted using a fractal model for classification in this method. With this technique, the histogram of a picture is modeled after a Gaussian distribution. Classification was performed with several SVM kernels, and the outcomes were compared. Step two proposes using a deep convolutional neural network (DCNN) architecture to identify Alzheimer's-related mental disorders. According to the findings, the SVM approach accurately recognized over 93% of the photos tested. The DCNN approach was one hundred percent accurate during model training, whereas the SVM approach achieved just 93 percent accuracy. In contrast to SVM's accuracy of 89.3%, the DCNN model test's findings were accurate 98.8% of the time. Based on the findings reported here, the proposed DCNN architecture may be used for diagnostic purposes involving the patient's mental state.

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“…In the testing phase, only the learned AGCNs are used for final prediction, as shown in Fig. 5 various tasks, such as skeleton-based human action recognition [32,29,39] and facial landmark-based emotion recognition [27,37,38]. In this study, we apply an adaptive GCNs (AGCN) [32], in which the topology of the graph can be learned, on the detected body joints and facial landmarks for seizure classification.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Stream Approach for Seizure Classification with Knowledge Distillation

Hou

McGonigal

Bartolomei

et al. 2021

2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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In this work, we propose a multi-stream approach with knowledge distillation to classify epileptic seizures and psychogenic non-epileptic seizures. The proposed framework utilizes multi-stream information from keypoints and appearance from both body and face. We take the detected keypoints through time as spatio-temporal graph and train it with an adaptive graph convolutional networks to model the spatio-temporal dynamics throughout the seizure event. Besides, we regularize the keypoint features with complementary information from the appearance stream by imposing a knowledge distillation mechanism. We demonstrate the effectiveness of our approach by conducting experiments on real-world seizure videos. The experiments are conducted by both seizure-wise cross validation and leaveone-subject-out validation, and with the proposed model, the performances of the F1-score/accuracy are 0.89/0.87 for seizure-wise cross validation, and 0.75/0.72 for leaveone-subject-out validation.

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Facial Expression Recognition using Convolutional Neural Network on Graphs

Cited by 16 publications

References 11 publications

Advances in Facial Expression Recognition: A Survey of Methods, Benchmarks, Models, and Datasets

Advances in Facial Expression Recognition: A Survey of Methods, Benchmarks, Models, and Datasets

Mathematical Model and Artificial Intelligence for Diagnosis of Alzheimer's Disease

A Multi-Stream Approach for Seizure Classification with Knowledge Distillation

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