Recent Facial Image Preprocessing Techniques: A Review †
Abstract
1. Introduction
2. Facial Image Recognition and Classification Algorithm
2.1. Facial Image Recognition
Ref. | Algorithm | PR | FE | FS | Remark |
---|---|---|---|---|---|
[2] | CNN | √ | √ | - | Highly effective under diverse lighting |
[8] | Multi-view Co-evolutionary Binary Optimization, and DNN | √ | √ | - | Good performance on occluded/noisy images |
[9] | HOG, SIFT, Gabor, Canny, and CNN | √ | √ | - | 100% accuracy with SIFT + CNN combination |
[10] | SAACS | - | √ | - | High accuracy |
[11] | DTCWT and Pseudo-Zernike Moments | √ | √ | - | Demonstrates strong resistance to geometric and noise-based attacks |
[7] | SVM and CNN (VGG-16) | - | √ | - | Quality and the number of data training greatly affect the training process |
[3] | CNN, LBPH, and HOG | - | √ | - | The CNN outperformed other benchmark algorithms |
[4] | LBPH and OpenFace DNN | - | √ | - | Superior accuracy compared to benchmark algorithms |
[6] | Few-Shot Learning | - | - | - | Enhances facial expression recognition efficiency |
[5] | Auto-Encoder with Skip Connections | - | √ | - | Improves occluded expression recognition |
[16] | Single-Shot Multibox Detector and Autoencoder | √ | √ | - | Uses an autoencoder that only utilizes the encoder part |
[12] | Dual LCDRC | √ | √ | - | High Accuracy: 98.86% |
[13] | DTCWT and Collaborative Representation Classifier | √ | √ | - | Robust to noise and invariant to lighting |
[14] | VGG-16 and Random Fourier Features | √ | √ | - | Effective on masked faces and consistent performance on various datasets with different pose and lighting characteristics |
[15] | Histogram of Enhanced Gradients and HOG | √ | √ | - | Significantly reduces the influence of noise through an adaptive denoising process, resulting in a high level of accuracy even in noisy images |
2.2. Facial Image Classification
3. Facial Image Preprocessing Technique
4. Future Direction
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Ref | Algorithm | Learning Methods | PR | FE | FS | Remark | |
---|---|---|---|---|---|---|---|
S | US | ||||||
[17] | Gabor Wavelets | √ | - | √ | √ | - | Accuracy is acceptable, with a success rate above 98.7% |
[20] | Histogram Equalization and CNN | √ | - | √ | √ | √ | 100% accuracy |
[21] | Haar Cascade HOGs and CNN | √ | - | √ | √ | - | Highly accurate and efficient for student validation during examinations |
[22] | Gabor Filters, Maximum Response, Monte Carlo, Uninformative Variable Elimination, and PLSR | √ | - | √ | √ | - | Able to process unstructured data. Low computation costs and time |
[23] | LMP and CNN-LCDRC | √ | - | - | √ | - | Robust to complex data |
[24] | DTCWT | √ | - | - | √ | - | Not suitable for asynchronous transmission |
[25] | Haar Cascade and DNN | √ | - | √ | √ | √ | Good for feature extraction. Large dataset scalability |
[26] | CNN-HOG | √ | - | - | √ | - | High accuracy. Potential for special scripts |
[10] | PSO and ACO | - | √ | - | - | √ | Sensitive to noise; may produce false edges |
[28] | Linear Discriminant Analysis | √ | - | √ | √ | - | Important information can be lost if dimensions are reduced too much |
[1] | PCA and Collaborative-Representation-Based Classification | √ | - | - | √ | √ | Improves accuracy with fewer samples |
[29] | PCA and GA | √ | - | - | √ | √ | GA achieves the smallest feature set |
[18] | Geodesic Path Algorithm, PCA, K-Nearest Neighbors, and SVM | √ | - | - | √ | √ | The geodesic path improves classification accuracy |
[19] | Self-Adaptive ACS and ACS | √ | - | - | - | √ | Classification accuracy up to 95.64 |
Ref. | Algorithm /Technique (Image Domain) | Preprocessing Activity | |||||||
---|---|---|---|---|---|---|---|---|---|
NZ | NR | IC | AL | RE | DA | IS | ED | ||
[20] | CNN (Digital images) | - | - | √ | - | - | - | - | - |
[21] | Gabor Wavelets (Facial image for real-time face detection) | √ | - | √ | √ | - | - | - | - |
[22] | Multi-view Co-evolutionary Binary Optimization and DNN (2D facial image) | √ | √ | √ | - | - | √ | - | √ |
[23] | Histogram Equalization and CNN (Grayscale facial image for robust recognition across different lighting conditions, poses, and facial expressions) | √ | √ | - | - | - | - | - | - |
[24] | Haar Cascade, HOGs, and CNN (2D real-time facial for recognition authentication in smart door lock systems) | √ | √ | √ | - | - | - | - | - |
[25] | Gabor Filters, Maximum Response, Monte Carlo Uninformative, Variable Elimination, and PLSR (2D facial images for real-world face detection and recognition) | √ | - | - | - | - | - | - | - |
[10] | SAACS (Ground-based sky images for cloud type classification) | √ | - | - | - | - | - | - | - |
[26] | DTCWT (2D facial image) | - | - | √ | - | - | - | - | √ |
[11] | HOG, SIFT, Gabor, Canny, and CNN (Digital multimedia images—watermarked images, logos, fingerprints, and facial images) | √ | √ | - | √ | - | - | ||
[7] | Haar Cascade and DNN (Labelled wild facial images) | - | - | - | - | - | √ | - | - |
[27] | PSO (Eye for biometric authentication) | √ | √ | - | - | - | - | √ | √ |
[28] | CNN-HOG (Fruit, frontal face, face attribute, and labelled facial images) | - | - | - | √ | - | - | - | - |
[29] | DTCWT and Pseudo-Zernike Moments (Fingerprint for digital signature) | √ | - | - | √ | - | - | - | - |
[34] | SVM amd CNN (VGG-16) (2D facial image to evaluate facial asymmetry) | √ | - | - | √ | - | - | √ | - |
[36] | CNN-LSTM (2D facial image) | - | √ | - | - | √ | - | - | √ |
[33] | PCA + Gabor and PCA + LBP (Facial expression) | - | √ | - | - | - | - | - | √ |
[37] | PCA and Collaborative-Representation-Based Classification (Face with sunglasses and scarf occlusion) | - | √ | - | - | - | - | - | - |
[30] | PCA and GA (3D facial expression and virtual facial expression) | √ | √ | - | √ | - | - | √ | - |
[38] | CNN, LBPH, and HOGs (Radboud faces) | √ | - | - | - | - | √ | - | - |
[32] | LBPH (Extended Yale Face B) | √ | - | √ | √ | - | √ | - | √ |
[31] | HOGs and landmark detection | √ | - | - | √ | - | - | √ | - |
[39] | Auto-Encoder with Skip Connections (Facial expression) | √ | √ | √ | √ | - | - | - | - |
[35] | Geodesic Path Algorithm, PCA, K-Nearest Neighbors, and SVM (Facial Recognition) | - | - | √ | - | - | √ | √ | √ |
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Soekarta, R.; Ku-Mahamud, K.R. Recent Facial Image Preprocessing Techniques: A Review. Eng. Proc. 2025, 84, 39. https://doi.org/10.3390/engproc2025084039
Soekarta R, Ku-Mahamud KR. Recent Facial Image Preprocessing Techniques: A Review. Engineering Proceedings. 2025; 84(1):39. https://doi.org/10.3390/engproc2025084039
Chicago/Turabian StyleSoekarta, Rendra, and Ku Ruhana Ku-Mahamud. 2025. "Recent Facial Image Preprocessing Techniques: A Review" Engineering Proceedings 84, no. 1: 39. https://doi.org/10.3390/engproc2025084039
APA StyleSoekarta, R., & Ku-Mahamud, K. R. (2025). Recent Facial Image Preprocessing Techniques: A Review. Engineering Proceedings, 84(1), 39. https://doi.org/10.3390/engproc2025084039