Review on Facial-Recognition-Based Applications in Disease Diagnosis
Abstract
:1. Introduction
2. The Facial Recognition System: Approaches and Algorithms
2.1. Image Capture
2.2. Workflow of Facial Recognition Technology
2.3. Facial Analysis Algorithms
2.3.1. Traditional Methods
2.3.2. Deep Learning Methods
2.3.3. Mature Software
3. The Facial Recognition System: Applications and Advantages
3.1. Performance in Varieties of Disease
3.1.1. Endocrine and Metabolic Diseases
3.1.2. Genetic and Chromosome Abnormalities
3.1.3. Neuromuscular Diseases
3.1.4. Other Types of Disease
3.2. Clinical Applications
3.3. Advantages over Traditional Methods
3.3.1. Accurate and Objective
3.3.2. Comprehensive and Informative
3.3.3. Improvement of Healthcare System
4. Future Outlook
4.1. Expansion of Database Volume
4.2. Factors Affecting Diagnostic Accuracy
4.3. Integration of Novel Technology
4.4. Applications beyond Diagnosis
4.5. From Research to Products
4.6. Privacy and Security
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Category | Algorithm |
---|---|
Appearance-based | Principal Component Analysis (PCA) Eigenface-Based Methods PCA Algorithm Kernel Principal Component Analysis (Kernel PCA) 2D-Image Principal Component Analysis (2D Image IPCA) Linear Discriminant Analysis (LDA) Discriminant Common Vectors (DCV) Independent Component Analysis (ICA) IPCA-ICA Super Vector Machine (SVM) |
Feature-based | Geometric Features Local Binary Patterns (LBP) Elastic Bunch Graph Matching (EBGM) Histogram of Oriented Gradients (HoG) Elastic Bunch Graph (EBG) Hidden Markov Model (HMM) |
Deep learning | Probabilistic-Decision-Based Neural Networks (PDBNN) Radial Basis Function (RBF) Convolutional Neural Network (CNN) |
Study | Disease | Method | Sample Size | Efficacy |
---|---|---|---|---|
Basel-Vanagaite et al. [46] | Cornelia de Lange syndrome | FDNA | 31 cases in training set, 17 cases in testing set | Accuracy = 87% (training), accuracy = 94% (testing) |
Latorre-Pellicer et al. [47] | Cornelia de Lange syndrome | Face2Gene | 49 cases | Accuracy = 83.7% |
Hadj-Rabia et al. [48] | X-linked hypohidrotic ectodermal dysplasia | Face2Gene | 136 cases, 717 controls | AUC ≥ 0.98 |
Liehr et al. [49] | Emanuel syndrome (ES) Pallister-Killian syndrome (PKS) | Face2Gene | 59 ES, 70 PKS, 973 controls, 973 others | AUC ≥ 0.98 |
Amudhavalli et al. [50] | Aymé-Gripp syndrome | Face2Gene | 13 cases, 20 controls, 20 DS | AUC = 0.994 (controls), AUC = 0.994 (DS) |
Pode-Shakked et al. [51] | Mucolipidosis type IV | Face2Gene | 26 cases, 98 controls, 99 others | AUC = 0.822 (controls), AUC = 0.885 (others) |
Wang et al. [52] | Kabuki syndrome | Face2Gene | 14 cases | Accuracy = 93% |
AbdAlmageed et al. [21] | Congenital adrenal hyperplasia | DNN | 102 cases, 144 controls | AUC = 92% |
Porras et al. [53] | Noonan syndrome (NS) Williams-Beuren syndrome (WBS) | LBP, SVM | 286 NS, 161 WBS | Accuracy = 85.68% |
Study | Disease | Data | Sample Size | Method | Efficacy |
---|---|---|---|---|---|
Bandini et al. [60] | PD | Video | 17 PD, 17 HC | Intraface tracking algorithm, Euclidean distance, SVM | Difference (p < 0.05) between PD and HC |
Rajnoha et al. [61] | PD | Image | 50 PD, 50 HC | Random Forests, XGBoost | Accuracy = 67.33% |
Jin et al. [23] | PD | Video | 33 PD, 31 HC | Face++ [62], tremor extraction, LSTM neural network | Precision = 86% |
Ali et al. [5] | PD | Video | 61 PD, 543 HC | OpenFace 2.0 [24], SVM | Accuracy = 95.6% |
Hou et al. [63] | PD | Video | 70 PD, 70 HC | HOG, LBP, SVM, k-NN, Random Forests | F1 = 88% |
Nam et al. [25] | AD | Video | 17 AD, 17 HC | OpenFace 2.0 [24], extract movement coordinates to calculate Spearman’s correlation coefficient | Difference (p < 0.05) between AD and HC |
Umeda et al. [64] | AD | Image | 121 AD, 117 HC | Xception, SENet50, ResNet50, VGG16, and simple CNN with SGD and Adam optimizer | Xception with Adam showed the best accuracy = 94% |
Bandini et al. [18] | ALS | Video | 11 ALS, 11 HC | AAM, CLM, ERT, SDM, FAN | Accuracy = 88.9% |
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Qiang, J.; Wu, D.; Du, H.; Zhu, H.; Chen, S.; Pan, H. Review on Facial-Recognition-Based Applications in Disease Diagnosis. Bioengineering 2022, 9, 273. https://doi.org/10.3390/bioengineering9070273
Qiang J, Wu D, Du H, Zhu H, Chen S, Pan H. Review on Facial-Recognition-Based Applications in Disease Diagnosis. Bioengineering. 2022; 9(7):273. https://doi.org/10.3390/bioengineering9070273
Chicago/Turabian StyleQiang, Jiaqi, Danning Wu, Hanze Du, Huijuan Zhu, Shi Chen, and Hui Pan. 2022. "Review on Facial-Recognition-Based Applications in Disease Diagnosis" Bioengineering 9, no. 7: 273. https://doi.org/10.3390/bioengineering9070273