DCGAN Beyond Generation: A Critical Review of The Performance and Challenges of Forensic Face Models
Keywords:
Deep Learning, Generative Models, Face Image ReconstructionAbstract
Computer vision and deep learning techniques, especially deep convolutional generative adversarial networks (DCGAN), have enabled advanced mechanisms to address complex challenges in forensics, especially in reactivating cold case investigations. Cold cases present unresolved challenges due to deteriorating or scarce visual evidence. This paper provides a systematic review that analyzes, classifies, and evaluates the current status of DCGAN and related GAN structures in legitimate face modeling. The primary objectives are to evaluate reported methodologies, performance metrics, and limitations across key applications, including sketch-to-image conversion. The review identifies significant methodological gaps, particularly the absence of standardized assessment measures and the critical challenge of identity preservation. Furthermore, the research explores the ethical and legal considerations associated with computer-generated facial images, focusing on algorithmic bias, accountability, and legal admissibility in criminal investigations. The paper concludes by highlighting key research gaps and proposing future directions necessary for the robust, reliable, and ethically responsible deployment of GAN systems in legitimate practice
Downloads
References
I. Goodfellow et al., “Generative adversarial networks,” Commun ACM, vol. 63, no. 11, pp. 139–144, Oct. 2020, doi: 10.1145/3422622.
S. Nikkath Bushra and K. Uma Maheswari, “Crime Investigation using DCGAN by Forensic Sketch-to-Face Transformation (STF)- A Review,” in Proceedings - 5th International Conference on Computing Methodologies and Communication, ICCMC 2021, Institute of Electrical and Electronics Engineers Inc., Apr. 2021, pp. 1343–1348. doi: 10.1109/ICCMC51019.2021.9418417.
C. Epskamp-Dudink and J. M. Winter, “Benefits of scenario reconstruction in cold case investigations,” J Crim Psychol, vol. 10, no. 2, pp. 65–78, May 2020, doi: 10.1108/JCP-09-2019-0035.
H. SABAH Al-Muttairi, “INVIGORATE COLD CASE INVESTIGATIONS BASED ON HUMAN FACE GENERATION USING DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORK (DCGAN)”, doi: 10.13140/RG.2.2.31791.44962.
A. Radford, L. Metz, and S. Chintala, “Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks,” Nov. 2015, [Online]. Available: http://arxiv.org/abs/1511.06434
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. Courville, “Improved Training of Wasserstein GANs,” pp. 5769–5779, Dec. 2017, doi: 10.48550/arXiv.1704.00028.
A. Borji, “Pros and Cons of GAN Evaluation Measures,” Feb. 2018, [Online]. Available: http://arxiv.org/abs/1802.03446
T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive growing of GANs for improved quality, stability, and variation,” in 6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings, International Conference on Learning Representations, ICLR, 2018.
H. S. K. AL-Muttairi, S. Kurnaz, and A. F. Aljuboori, “Enhancing Cold Cases Forensic Identification with DCGAN-based Personal Image Reconstruction,” Baghdad Science Journal, vol. 22, no. 2, pp. 730–739, 2025, doi: 10.21123/bsj.2024.10896.
S. Ammar, T. Bouwmans, and M. Neji, “Face Identification Using Data Augmentation Based on the Combination of DCGANs and Basic Manipulations,” Information (Switzerland), vol. 13, no. 8, Aug. 2022, doi: 10.3390/info13080370.
Q. Li et al., “AF-DCGAN: Amplitude Feature Deep Convolutional GAN for Fingerprint Construction in Indoor Localization Systems,” IEEE Trans Emerg Top Comput Intell, vol. 5, no. 3, pp. 468–480, Jun. 2021, doi: 10.1109/TETCI.2019.2948058.
S. Mullery and P. F. Whelan, “Batch Normalization in the final layer of generative networks,” CoRR , vol. abs/1803.08494, May 2018, doi: https://doi.org/10.48550/arXiv.1805.07389.
R. Sindhu, K. Prathyusha, S. Ravi, and M. C. Suman, “Implementation of Digital Forensics Face Sketch Recognition using Fusion Based Deep Learning Convolution Neural Network,” in Proceedings of the 5th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2021, Institute of Electrical and Electronics Engineers Inc., 2021, pp. 1499–1504. doi: 10.1109/ICECA52323.2021.9676004.
C. Galea and R. A. Farrugia, “Forensic Face Photo-Sketch Recognition Using a Deep Learning-Based Architecture,” IEEE Signal Process Lett, vol. 24, no. 11, pp. 1586–1590, Nov. 2017, doi: 10.1109/LSP.2017.2749266.
D. Chaves, E. Fidalgo, E. Alegre, R. Alaiz-Rodríguez, F. Jáñez-Martino, and G. Azzopardi, “Assessment and estimation of face detection performance based on deep learning for forensic applications,” Sensors (Switzerland), vol. 20, no. 16, pp. 1–21, Aug. 2020, doi: 10.3390/s20164491.
J. Li et al., “Feature selection: A data perspective,” Nov. 30, 2017, Association for Computing Machinery. doi: 10.1145/3136625.
S. Du and R. Ward, “Wavelet-based illumination normalization for face recognition,” in Proceedings - International Conference on Image Processing, ICIP, 2005, pp. 954–957. doi: 10.1109/ICIP.2005.1530215.
H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “Speeded-Up Robust Features (SURF),” Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346–359, Jun. 2008, doi: 10.1016/j.cviu.2007.09.014.
G. Antipov, S. A. Berrani, N. Ruchaud, and J. L. Dugelay, “Learned vs hand-crafted features for pedestrian gender recognition,” in MM 2015 - Proceedings of the 2015 ACM Multimedia Conference, Association for Computing Machinery, Inc, Oct. 2015, pp. 1263–1266. doi: 10.1145/2733373.2806332.
X. Y. Wang, J. F. Wu, and H. Y. Yang, “Robust image retrieval based on color histogram of local feature regions,” Multimed Tools Appl, vol. 49, no. 2, pp. 323–345, Aug. 2010, doi: 10.1007/s11042-009-0362-0.
M. Hassaballah, A. A. Abdelmgeid, and H. A. Alshazly, “Image features detection, description and matching,” Studies in Computational Intelligence, vol. 630, pp. 11–45, 2016, doi: 10.1007/978-3-319-28854-3_2.
R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, Sep. 2014, pp. 580–587. doi: 10.1109/CVPR.2014.81.
W. Liu, Y. Gu, and K. Zhang, “Face Generation using DCGAN for Low Computing Resources,” in Proceedings - 2021 2nd International Conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2021, Institute of Electrical and Electronics Engineers Inc., 2021, pp. 377–382. doi: 10.1109/ICBASE53849.2021.00076.
X. Yin, B. Hou, Y. Huang, C. Li, Z. Fan, and J. Liu, “Image Enhancement Method Based on Improved DCGAN for Limit Sample,” in Proceedings - 2022 14th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2022, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 376–379. doi: 10.1109/ICMTMA54903.2022.00078.
S. Li, “The study for optimization strategies on the performance of DCGAN,” in Journal of Physics: Conference Series, Institute of Physics, 2023. doi: 10.1088/1742-6596/2634/1/012032.
C. Koç and F. Özyurt, “An examination of synthetic images produced with DCGAN according to the size of data and epoch,” FIRAT UNIVERSITY JOURNAL OF EXPERIMENTAL AND COMPUTATIONAL ENGINEERING, vol. 2, no. 1, pp. 32–37, 2023, doi: 10.5505/fujece.2023.69885.
B. Mandal, N. B. Puhan, and A. Verma, “Deep Convolutional Generative Adversarial Network Based Food Recognition Using Partially Labeled Data,” Dec. 2018, [Online]. Available: http://arxiv.org/abs/1812.10179
N. M. K. Arnob, N. N. Rahman, S. Mahmud, Md. N. Uddin, R. Rahman, and A. K. Saha, “Facial Image Generation from Bangla Textual Description using DCGAN and Bangla FastText,” International Journal of Advanced Computer Science and Applications, vol. 14, no. 6, p. 2023, 2023, doi: 10.14569/IJACSA.2023.01406134.
S. Liu, M. Yu, M. Li, and Q. Xu, “The research of virtual face based on Deep Convolutional Generative Adversarial Networks using TensorFlow,” Physica A: Statistical Mechanics and its Applications, vol. 521, 2019, doi: 10.1016/j.physa.2019.01.036.
A. Jadli, M. Hain, A. Chergui, and A. Jaize, “DCGAN-based data augmentation for document classification,” in 2020 IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science, ICECOCS 2020, Institute of Electrical and Electronics Engineers Inc., Dec. 2020. doi: 10.1109/ICECOCS50124.2020.9314379.
J. Lehtinen and T. Aila NVIDIA, “Analyzing and Improving the Image Quality of StyleGAN Tero Karras NVIDIA Samuli Laine NVIDIA Miika Aittala NVIDIA Janne Hellsten NVIDIA.” doi: https://doi.org/10.48550/arXiv.1912.04958.
H. Ni and W. Su, “A Self-Attention CycleGAN for Unsupervised Image Hazing,” Big Data and Cognitive Computing, vol. 9, no. 4, Apr. 2025, doi: 10.3390/bdcc9040096.
Published
How to Cite
Issue
Section
Copyright (c) 2025 HASAN AL-MUTTAIRI; Mr. Mahmood
This work is licensed under a Creative Commons Attribution 4.0 International License.
