Cross-Spectral Cross-Distance Face Recognition via CNN with Image Augmentation Techniques
Abstract
Facial recognition is a critical biometric identification method in modern security systems, yet it faces significant challenges under varying lighting conditions, particularly when dealing with near-infrared (NIR) images, which exhibit reduced illumination compared to visible light (VIS) images. This study aims to evaluate the performance of Convolutional Neural Networks (CNNs) in addressing the Cross-Spectral Cross-Distance (CSCD) challenge, which involves face identification across different spectra (NIR and VIS) and varying distances. Three CNN models—VGG16, ResNet50, and EfficientNetB0—were assessed using a dataset comprising 800 facial images from 100 individuals, captured at four different distances (1m, 60m, 100m, and 150m) and across two wavelengths (NIR and VIS). The Multi-task Cascaded Convolutional Networks (MTCNN) algorithm was employed for face detection, followed by image preprocessing steps including resizing to 224x224 pixels, normalization, and homomorphic filtering. Two distinct data augmentation strategies were applied: one utilizing 10 different augmentation techniques and the other with 4 techniques, trained with a batch size of 32 over 100 epochs. Among the tested models, VGG16 demonstrated superior performance, achieving 100% accuracy in both training and validation phases, with a training loss of 0.55 and a validation loss of 0.612. These findings underscore the robustness of VGG16 in effectively adapting to the CSCD setting and managing variations in both lighting and distance.
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References
K. Guo, S. Wu, and Y. Xu, "Face Recognition Using Both Visible Light Image and Near-infrared Image and a Deep Network," CAAI Transactions on Intelligence Technology, Mar 2017, vol. 2, pp. 39-47.
A. Quagllia and C. M. Epifano, Face Recognition: Methods, Applications and Technology, 2012.
F. Arnia, M. Oktiana, et al., "Homomorphic Filtering and Phase-Based Matching for Cross-Spectral Cross-Distance Face Recognition," MDPI, 2021.
M. Oktiana, T. Horiuchi, K. Hirai, K. Saddami, F. Arnia, Y. Away, K. Munadi, “Cross-spectral iris recognition using phase-based matching and homomorphic filtering”, Heliyon, 2020.
F. Juefei-Xu, D. K. Pal and M. Savvides, "NIR-VIS Heterogeneous Face Recognition via Cross-Spectral Joint Dictionary Learning and Reconstruction," IEEE Conference on Computer Vision and Pattern Recognition Workshops, Providence, RI, USA, 2015, pp. 141-150, doi: 10.1109/CVPRW.2015.7301308.
J. Lezama, Q. Qiu and G. Sapiro, "Not Afraid of the Dark: NIR-VIS Face Recognition via Cross-Spectral Hallucination and Low-Rank Embedding," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp. 6807-6816, doi: 10.1109/CVPR.2017.720.
R. He, J. Cao, L. Song, Z. Sun and T. Tan, "Adversarial Cross-Spectral Face Completion for NIR-VIS Face Recognition," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no. 5, pp. 1025-1037, 1 May 2020, doi: 10.1109/TPAMI.2019.2961900.
M. Wang and W. Deng, "Deep face recognition: A survey," Neurocomputing, vol. 429, pp. 215-244, 2021, doi: 10.1016/j.neucom.2020.10.081.
R. Jiang, C. T. Li, D. Crookes, W. Meng, and C. Rosenberger, "Deep Learning Models for Face Recognition: A Comparative Analysis," Unsupervised and Semi-Supervised Learning, Eds. Cham, Switzerland: Springer, 2020, pp. 123-140. doi: 10.1007/978-3-030-32583-1_6.
G. Guo and N. Zhang, "A survey on deep learning based face recognition," Computer Vision and Image Understanding, vol. 189, 2019, doi: 10.1016/j.cviu.2019.102805.
J. Aul, A. Roy, S. Mallick, and J. Sil, "A Comparative Study of Deep Learning-Based Face Recognition and Emotion Detection Techniques Using Social Media Customized Cartoon Post," Computational Intelligence in Pattern Recognition, Springer, 2023, vol. 725, pp. 389-400. doi: 10.1007/978-981-99-3734-9_33.
A. Kebaili, J. Lapuyade-Lahorgue and S. Ruan, “Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review”, National Library of Medicine, April 2023, Vol. 9, No.4, doi: https://doi.org/10.3390/jimaging9040081.
E. Shorten, T. M. Khoshgoftaar, "A survey on Image Data Augmentation for Deep Learning," Journal of Big Data, vol. 6, no. 60, pp. 1-48, 2019.
V. Uchoa, K. Aires, R. Veras, A. Paiva, and L. Britto, "Data Augmentation for Face Recognition with CNN Transfer Learning," 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), 2020, pp. 143-148, doi: 10.1109/IWSSIP48289.2020.9145125.
D. Shamia, D. Abraham Chandy, "Intelligent System for Cross-Spectral and Cross-Distance Face Matching," Computers & Electrical Engineering, vol. 67, pp. 228-239, November 2017.
H. Ku and W. Dong, “Face Recognition Based on MTCNN and Convolutional Neural Network”, Frontiers in Signal Processing, January 2020, vol. 4, no. 1, doi: https://dx.doi.org/10.22606/fsp.2020.41006.
M. Yuan, S. Y. Nikouei, A. Fitwi, Y. Chen and Y. Dong, "Minor Privacy Protection Through Real-time Video Processing at the Edge," 2020 29th International Conference on Computer Communications and Networks (ICCCN), Honolulu, HI, USA, 2020, pp. 1-6, doi: 10.1109/ICCCN49398.2020.9209632.
Z. Yang, W. Ge and Z. Zhang, "Face Recognition Based on MTCNN and Integrated Application of FaceNet and LBP Method," 2020 2nd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM), Manchester, United Kingdom, 2020, pp. 95-98, doi: 10.1109/AIAM50918.2020.00024.
W. A. Mustafa, W. Khairunizam, H. Yazid, Z. Ibrahim, A. Shahriman, and Z. M. Razlan, "Image Correction Based on Homomorphic Filtering Approaches: A Study," in Proceedings of the IEEE International Conference on Computational Approach in Smart Systems Design and Applications (ICASSDA), pp. 1-5, 2018.
S. Gamini and S. S. Kumar, "Homomorphic filtering for image enhancement based on fractional-order derivative and genetic algorithm," Computers and Electrical Engineering, vol. 106, 2023, doi: 10.1016/j.compeleceng.2022.108566.
Á. Chavarín, E. Cuevas, O. Avalos, J. Gálvez and M. Pérez-Cisneros, "Contrast Enhancement in Images by Homomorphic Filtering and Cluster-Chaotic Optimization," in IEEE Access, vol. 11, pp. 73803-73822, 2023, doi: 10.1109/ACCESS.2023.3287559.
M. Oktiana, T. Horiuchi, K. Hirai, K. Saddami, F. Arnia, Y. Away, and K. Munadi, "Cross-spectral iris recognition using phase-based matching and homomorphic filtering," Heliyon, vol. 6, no. 10, Oct. 2020.
F. M. Salman and S. S. Abu-Naser, "Classification of Real and Fake Human Faces Using Deep Learning," International Journal of Academic Engineering Research (IJAER), vol. 6, no. 3, pp. 1-14, Mar. 2022.
K. Alomar, H. I. Aysel dan X. Cai, “Data Augmentation in Classification and Segmentation: A Survey and New Strategies”, Journal of Imaging, 2023, Vol. 9, No. 2, p. 46, doi: https://doi.org/10.3390/jimaging9020046
Francois Chollet, Deep Learning with Python, Second Edition , Manning, 2021.
M. Xu, S. Yoon, A. Fuentes, and D. S. Park, "A Comprehensive Survey of Image Augmentation Techniques for Deep Learning," Pattern Recognition, vol. 137, pp. 109347, 2023. doi: 10.1016/j.patcog.2023.109347.
S. Lasniari, Jasril, S. Sanjaya, F. Yanto, and M. Affandes, "Klasifikasi Citra Daging Babi dan Daging Sapi Menggunakan Deep Learning Arsitektur ResNet-50 dengan Augmentasi Citra," Jurnal Penelitian Teknologi dan Rekayasa, vol. 3, no. 4, pp. 45-58, 2022, doi: 10.1234/jptr.v5i2.1234.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov and L. -C. Chen, "MobileNetV2: Inverted Residuals and Linear Bottlenecks," 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp. 4510-4520, doi: 10.1109/CVPR.2018.00474.
L. Ali, F. Alnajjar, H. A. Jassmi, M. Gocho, W. Khan, and M. A. Serhani, “Performance Evaluation of Deep CNN-Based Crack Detection and Localization Techniques for Concrete Structures,” Sensors, 2021, vol. 21, no. 5, p. 1688, doi : https://doi.org/10.3390/s21051688.
N. Choudhary, A. Sharma, V. S. Rathore, dan N. Tiwari, "Performance Comparison of ResNet50V2 and VGG16 Models for Feature Extraction in Deep Learning," SpringerLink, 2024, pp. 223-229, doi: https://doi.org/10.1007/978-981-99-8031-4_21.
A. Shazia, T. Z. Xuan, J. H. Chuah, et al., "A comparative study of multiple neural network for detection of COVID-19 on chest X-ray," EURASIP J. Adv. Signal Process, 2021, no. 50, doi: 10.1186/s13634-021-00755-1.
S. Asif, Q. u. Ain, S. U. R. Khan, et al., "SKINC-NET: an efficient Lightweight Deep Learning Model for Multiclass skin lesion classification in dermoscopic images," Multimed Tools Appl, 2024. doi: 10.1007/s11042-024-19489-x.
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