Authors : Hossenbux Muhammad Yaaseen

Volume/Issue : Volume 11 - 2026, Issue 2 - February

Google Scholar : https://tinyurl.com/3s3r835z

Scribd : https://tinyurl.com/mrxmx92z

DOI : https://doi.org/10.38124/ijisrt/26feb145

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : In recent years, several researchers have worked to lessen biasness in face recognition systems since it had produced numerous problems, from people getting falsely accused of minor offences up to murder, getting wrongfully arrested and in some extreme cases, getting killed, and these happened particularly for people of colour. This thesis aims to contribute to the data science field by investigating and implementing generative models as a potential assistance to expand the diversity of datasets used in facial recognition technologies and help mitigate racial biasness in facial recognition systems. The CelebA Dataset, containing more than 100,000 unique photos, was utilized to train our StyleGAN 2 model, to generate synthetic realistic images and the FairFace dataset which has a diverse dataset over 100k images of both males and females for our recognition model. East Asian, African, Caucasian, Indian, Middle Eastern, Latino, and Southeast Asian are the racial categories that have been identified. We used InceptionResnetV1 for feature extraction, MTCNN for face detection and then ran our recognition Model on our diverse dataset with and without synthetic images. After using our own generated synthethic data, we saw accuracy gains for a few of the races, including East Asian, African, Indian, Middle Eastern, and Latino, which demonstrated that the accuracy level increased by more than 10% in some cases.

References :

18. Joy Buolamwini and Timnit Gebru, "Gender shades: Intersectional accuracy disparities in commercial gender classification", Conference on fairness accountability and transparency, pp. 77-91, 2018.
19. Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman and Aram Galstyan, "A survey on bias and fairness in machine learning", ACM Computing Surveys (CSUR), vol. 54, no. 6, pp. 1-35, 2021.
20. Shruti Nagpal, Maneet Singh, Richa Singh and Mayank Vatsa, "Deep learning for face recognition: Pride or prejudiced?", arXiv preprint arXiv:1904.01219, 2019.
21. Harini Suresh and John V Guttag, "A framework for understanding unintended consequences of machine learning", arXiv preprint arXiv:1901.10002, 2019.
22. Mei Wang, Weihong Deng, Jiani Hu, Xunqiang Tao and Yaohai Huang, "Racial faces in the wild: Reducing racial bias by information maximization adaptation network", The IEEE International Conference on Computer Vision (ICCV), October 2019.
23. Sixue Gong, Xiaoming Liu and Anil K Jain, "Mitigating face recognition bias via group adaptive classifier", Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3414-3424, 2021
24. Yuning Qiu, Teruhisa Misu, Carlos Busso, “Unsupervised Scalable Multimodal Driving Anomaly Detection”, IEEE
25. TRANSACTIONS ON INTELLIGENT VEHICLES, VOL. 8, NO. 4, APRIL 2023
26. WEI LI , JINBAO SUN2 , JING ZHANG , BOCHENG ZHANG, “Face Recognition Model Optimization Research Based on Embedded Platform”, DOI: 10.1109/ACCESS.2023.3277495 ,June 2023
27. J. Engel, V. Koltun and D. Cremers, "Direct Sparse Odometry," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 3, pp. 611-625, 1 March 2018, doi: 10.1109/TPAMI.2017.2658577.
28. H. Mav, A. Mokashi, S. Nanduri and V. Pinjarkar, "Face Recognition and Adversarial Masking Techniques," 2022 3rd International Conference for Emerging Technology (INCET), Belgaum, India, 2022, pp. 1-7, doi: 10.1109/INCET54531.2022.9825044.
29. Seyma Yucer, Samet Akçay, Noura Al-Moubayed and Toby P Breckon, "Exploring racial bias within face recognition via per-subject adversarially-enabled data augmentation", Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 18-19, 2020.
30. Zeyu Wang, Klint Qinami, Ioannis Christos Karakozis, Kyle Genova, Prem Nair, Kenji Hata, et al., "Towards fairness in visual recognition: Effective strategies for bias mitigation", Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 8919-8928, 2020.
31. M. Gwilliam, S. Hegde, L. Tinubu and A. Hanson, "Rethinking Common Assumptions to Mitigate Racial Bias in Face Recognition Datasets," 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Montreal, BC, Canada, 2021, pp. 4106-4115, doi: 10.1109/ICCVW54120.2021.00458.
32. N. Stojanović, Z. Qiang, C. Prodaniuc and F. Karinou, "Eye deskewing algorithms for PAM modulation formats in IM-DD transmission systems," 2017 Optical Fiber Communications Conference and Exhibition (OFC), Los Angeles, CA, USA, 2017, pp. 1-3.
33. M. Wang and W. Deng, "Mitigating Bias in Face Recognition Using Skewness-Aware Reinforcement Learning," 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2020, pp. 9319-9328, doi: 10.1109/CVPR42600.2020.00934.
34. Mei Wang, Weihong Deng, Jiani Hu, Xunqiang Tao and Yaohai Huang, "Racial faces in the wild: Reducing racial bias by information maximization adaptation network", Proceedings of the IEEE International Conference on Computer Vision, pp. 692-702, 2019.
35. James Zou and Londa Schiebinger, Ai can be sexist and racistlits time to make it fair, 2018.
36. Brendan F Klare, Mark J Burge, Joshua C Klontz, Richard W Vorder Bruegge and Anil K Jain, "Face recognition performance: Role of demographic information", IEEE Transactions on Information Forensics and Security, vol. 7, no. 6, pp. 1789-1801, 2012.
37. L. Huang et al., "Gradient Attention Balance Network: Mitigating Face Recognition Racial Bias via Gradient Attention," 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Vancouver, BC, Canada, 2023, pp. 38-47, doi: 10.1109/CVPRW59228.2023.00009.
38. Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun, "Deep residual learning for image recognition", Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778, 2016.
39. J. Ge, W. Deng, M. Wang and J. Hu, "FGAN: Fan-Shaped GAN for Racial Transformation," 2020 IEEE International Joint Conference on Biometrics (IJCB), Houston, TX, USA, 2020, pp. 1-7, doi: 10.1109/IJCB48548.2020.9304901.
    1. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, et al., "Generative adversarial networks", NIPS, 2014.
40. Y. Choi, M. Choi, M. Kim, J. Ha, S. Kim and J. Choo, "Stargan: Unified generative adversarial networks for multidomain image-to-image translation", CoRR, 2017
41. Y. Jo and J. Park, "SC-FEGAN: face editing generative adversarial network with user's sketch and color", CoRR, 2019.
42. P. Isola, J. Zhu, T. Zhou and A. A. Efros, "Image-to-image translation with conditional adversarial networks", CoRR, vol. abs, 2016.
43. M. Wang, W. Deng, J. Hu, J. Peng, X. Tao and Y. Huang, "Racial faces in-the-wild: Reducing racial bias by deep unsupervised domain adaptation", CoRR, 2018.
44. S. Yucer, F. Tektas, N. A. Moubayed and T. P. Breckon, "Measuring Hidden Bias within Face Recognition via Racial Phenotypes," 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, 2022,
45. pp. 3202-3211, doi: 10.1109/WACV51458.2022.00326.
46. Cynthia Feliciano, "Shades of race: How phenotype and observer characteristics shape racial classification", American Behavioral Scientist, 2016.
47. Abdulla Fakhro, Hyung Woo Yim, Yong Kyu Kim and Anh H Nguyen, "The evolution of looks and expectations of asian eyelid and eye appearance" in Seminars in plastic surgery., Thieme Medical Publishers, 2015.
48. Theiab Alzahrani, Waleed Al-Nuaimy and Baidaa Al-Bander, "Integrated multi-model face shape and eye attributes identification for hair style and eyelashes recommendation", Computation, 2021
49. Yoonho Lee, Euitae Lee and Won Jin Park, "Anchor epicanthoplasty combined with out-fold type double eyelidplasty for asians: do we have to make an additional scar to correct the asian epicanthal fold?", Plastic and reconstructive surgery, 2000.
50. Ziqing Zhuang, Douglas Landsittel, Stacey Benson, Raymond Roberge and Ronald Shaffer, "Facial anthropometric differences among gender ethnicity and age groups", Annals of occupational hygiene, 2010.
51. De La Mettrie, Didier Saint-Léger, Genevievève Loussouarn, Annelise Garcel, Crystal Porter and André Langaney, "Shape variability and classification of human hair: a worldwide approach", Human biology, 2007.
52. Jonathan L Rees, "Genetics of hair and skin color", Annual review of genetics, 2003.
53. https://www.chicagofaces.org/
54. https://www.kaggle.com/datasets/kleinertee/fairface

55. https://www.kaggle.com/datasets/jessicali9530/celeba-dataset