eISSN: 3087-4068 editor@aimjournals.com
All Journals
Submit your article

International Research Journal of Advanced Engineering and Technology

Open Access Peer Review International
Open Access

ENHANCING FACIAL IMAGE QUALITY: A REVIEW OF RECENT PREPROCESSING APPROACHES

DOI https://doi.org/10.55640/irjaet-v02i01-01
Miguel Dela Cruz 1 ,
4 Department of Computer Science, University of the Philippines, Quezon City, Philippines

Abstract

Facial image preprocessing plays a vital role in the performance of facial recognition systems and related applications, such as security, healthcare, and human-computer interaction. The quality of facial images is often compromised due to various factors, including lighting conditions, pose variations, occlusions, and noise. Therefore, efficient preprocessing techniques are essential for enhancing the clarity and usability of facial images in these systems. This review presents an overview of recent advancements in facial image preprocessing techniques, focusing on methods aimed at improving image quality, reducing noise, normalizing lighting, and handling occlusions. A detailed analysis of various preprocessing strategies—such as image enhancement, normalization, and alignment—has been provided, along with their advantages and challenges. The review highlights the importance of integrating deep learning methods with traditional image processing techniques to improve the overall performance of facial recognition systems. Furthermore, future trends and research directions in facial image preprocessing are discussed.

Keywords

Facial Image Processing, Preprocessing, Face Recognition

References

Yin, H.-F.; Wu, X.-J.; Hu, C.; Song, X. Face Recognition via Compact Second-Order Image Gradient Orientations. Mathematics 2022, 10, 2587. [Google Scholar] [CrossRef]
Shi, M.; Xu, L.; Chen, X. A Novel Facial Expression Intelligent Recognition Method Using Improved Convolutional Neural Network. IEEE Access 2020, 8, 57606–57614. [Google Scholar] [CrossRef]
Tsalera, E.; Papadakis, A.; Samarakou, M.; Voyiatzis, I. Feature Extraction with Handcrafted Methods and Convolutional Neural Networks for Facial Emotion Recognition. Appl. Sci. 2022, 12, 8455. [Google Scholar] [CrossRef]
Chen, Y.-C.; Liao, Y.-S.; Shen, H.-Y.; Syamsudin, M.; Shen, Y.-C. An Enhanced LBPH Approach to Ambient-Light-Affected Face Recognition Data in Sensor Network. Electronics 2022, 12, 166. [Google Scholar] [CrossRef]
Poux, D.; Allaert, B.; Ihaddadene, N.; Bilasco, I.M.; Djeraba, C.; Bennamoun, M. Dynamic Facial Expression Recognition Under Partial Occlusion With Optical Flow Reconstruction. IEEE Trans. Image Process. 2022, 31, 446–457. [Google Scholar] [CrossRef]
Kim, C.-L.; Kim, B.-G. Few-shot learning for facial expression recognition: A comprehensive survey. J. Real-Time Image Process. 2023, 20, 52. [Google Scholar] [CrossRef]
Van Nguyen, T.; Chu, T.D. Comparative study on the performance of face recognition algorithms. EUREKA Phys. Eng. 2023, 4, 120–132. [Google Scholar] [CrossRef]
Soni, N.; Sharma, E.K.; Kapoor, A. Hybrid meta-heuristic algorithm based deep neural network for face recognition. J. Comput. Sci. 2021, 51, 101352. [Google Scholar] [CrossRef]
Benradi, H.; Chater, A.; Lasfar, A. A hybrid approach for face recognition using a convolutional neural network combined with feature extraction techniques. IAES Int. J. Artif. Intell. 2023, 12, 627. [Google Scholar] [CrossRef]
Sagban, R.; Ku-Mahamud, K.R.; Bakar, M.S.A. ACO ustic: A nature-inspired exploration indicator for ant colony optimization. Sci. World J. 2015, 2015, 392345. [Google Scholar] [CrossRef] [PubMed]
Agilandeeswari, L.; Prabukumar, M.; Alenizi, F.A. A robust semi-fragile watermarking system using Pseudo-Zernike moments and dual tree complex wavelet transform for social media content authentication. Multimed. Tools Appl. 2023, 82, 43367–43419. [Google Scholar] [CrossRef]
Hosgurmath, S.; Mallappa, V.V.; Patil, N.B.; Petli, V. Effective face recognition using dual linear collaborative discriminant regression classification algorithm. Multimed. Tools Appl. 2022, 81, 6899–6922. [Google Scholar] [CrossRef]
Chen, G.Y.; Krzyżak, A.; Duda, P.; Cader, A. Noise Robust Illumination Invariant Face Recognition Via Bivariate Wavelet Shrinkage in Logarithm Domain. J. Artif. Intell. Soft Comput. Res. 2022, 12, 169–180. [Google Scholar] [CrossRef]
Sikha, O.K.; Bharath, B. VGG16-random fourier hybrid model for masked face recognition. Soft Comput. 2022, 26, 12795–12810. [Google Scholar] [CrossRef] [PubMed]
Berrimi, F.; Kara-Mohamed, C.; Hedli, R.; Hamdi-Cherif, A. The Histogram of Enhanced Gradients (HEG)—A Fast Descriptor for Noisy Face Recognition. Rev. d’Intelligence Artif. 2024, 38, 33–44. [Google Scholar] [CrossRef]
Sandhya, S.; Balasundaram, A.; Shaik, A. Deep Learning Based Face Detection and Identification of Criminal Suspects. Comput. Mater. Contin. 2023, 74, 2331–2343. [Google Scholar] [CrossRef]
Rashid, S.J.; Abdullah, A.I.; Shihab, M.A. Face Recognition System Based on Gabor Wavelets Transform, Principal Component Analysis and Support Vector Machine. Int. J. Adv. Sci. Eng. Inf. Technol. 2020, 10, 959–963. [Google Scholar] [CrossRef]
Marzoog, Z.S.; Hasan, A.D.; Abbas, H.H. Gender and race classification using geodesic distance measurement. Indones. J. Electr. Eng. Comput. Sci. 2022, 27, 820. [Google Scholar] [CrossRef]
Petwan, M.; Ku-Mahamud, K.R. Feature selection for sky image classification based on self adaptive ant colony system algorithm. Int. J. Electr. Comput. Eng. 2023, 13, 7037. [Google Scholar] [CrossRef]
Sukumaran, A.; Brindha, T. Optimal feature selection with hybrid classification for automatic face shape classification using fitness sorted Grey wolf update. Multimed. Tools Appl. 2021, 80, 25689–25710. [Google Scholar] [CrossRef]
Goud, K.M.; Hussain, S.J. Implementation of Invigilation System using Face Detection and Face Recognition Techniques. A Case Study. J. Eng. Sci. Technol. Rev. 2021, 14, 109–120. [Google Scholar] [CrossRef]
See, Y.-C.; Liew, E.; Noor, N.M. Gabor and Maximum Response Filters with Random Forest Classifier for Face Recognition in the Wild. Int. Arab J. Inf. Technol. 2021, 18, 797–806. [Google Scholar] [CrossRef]
Shi, D.; Tang, H. Face recognition algorithm based on self-adaptive blocking local binary pattern. Multimed. Tools Appl. 2021, 80, 23899–23921. [Google Scholar] [CrossRef]
Chen, Z.; Wu, X.-J.; Yin, H.-F.; Kittler, J. Noise-robust dictionary learning with slack block-Diagonal structure for face recognition. Pattern Recognit. 2020, 100, 107118. [Google Scholar] [CrossRef]
Muttaqin, R.; Fuada, S.; Mulyana, E. Attendance system using machine learning-based face detection for meeting room application. Int. J. Adv. Comput. Sci. Appl. 2020, 11, 286–293. [Google Scholar] [CrossRef]
Kadappa, R. Face Recognition Using Modified Histogram of Oriented Gradients and Convolutional Neural Networks. Int. J. Image Graph. Signal Process. 2023, 15, 60–76. [Google Scholar] [CrossRef]
Niu, S.; Nie, Z.; Liu, J.; Chu, M. An Application Study of Improved Iris Image Localization Based on an Evolutionary Algorithm. Electronics 2023, 12, 4454. [Google Scholar] [CrossRef]
Abusham, E.; Ibrahim, B.; Zia, K.; Rehman, M. Facial Image Encryption for Secure Face Recognition System. Electronics 2023, 12, 774. [Google Scholar] [CrossRef]
Perez-Gomez, V.; Rios-Figueroa, H.V.; Rechy-Ramirez, E.J.; Mezura-Montes, E.; Marin-Hernandez, A. Feature Selection on 2D and 3D Geometric Features to Improve Facial Expression Recognition. Sensors 2020, 20, 4847. [Google Scholar] [CrossRef] [PubMed]
Wazirali, R.; Ahmed, R. Hybrid Feature Extractions and CNN for Enhanced Periocular Identification During COVID-19. Comput. Syst. Sci. Eng. 2022, 41, 305–320. [Google Scholar] [CrossRef]
Farook, T.H.; Saad, F.H.; Ahmed, S.; Dudley, J. Dental Loop FLT: Facial landmark tracking. SoftwareX 2023, 24, 101531. [Google Scholar] [CrossRef]
Pei, X.; Zhao, Y.; Chen, L.; Guo, Q.; Duan, Z.; Pan, Y.; Hou, H. Robustness of machine learning to color, size change, normalization, and image enhancement on micrograph datasets with large sample differences. Mater. Des. 2023, 232, 112086. [Google Scholar] [CrossRef]
Jayasimha, Y.; Reddy, R.V.S. A facial expression recognition model using hybrid feature selection and support vector machines. Int. J. Inf. Comput. Secur. 2021, 14, 79. [Google Scholar] [CrossRef]
Harastani, M.; Benterkia, A.; Zadeh, F.M.; Nait-Ali, A. Methamphetamine drug abuse and addiction: Effects on face asymmetry. Comput. Biol. Med. 2020, 116, 103475. [Google Scholar] [CrossRef]
Attivissimo, F.; D’Alessandro, V.I.; Di Nisio, A.; Scarcelli, G.; Schumacher, J.; Lanzolla, A.M.L. Performance evaluation of image processing algorithms for eye blinking detection. Measurement 2023, 223, 113767. [Google Scholar] [CrossRef]

Most read articles by the same author(s)