Leon De França Nascimento
ABSTRACT
Context: Soft biometrics is a field that aids traditional biometrics through attribute identification using descriptors such as hair type, ethnicity, or gender. When employed to large search sets, allows optimization of the search space, thus increasing performance. Problem: Ethnicity classification is controversial due to unclear or stereotypical definitions used for classifying individuals, producing incorrect results. While there are many applications that use face images to ascertain ethnicity, no extensive study has been done to map these approaches. Solution: A literature survey of the strategies, data, and results used for ethnicity classification is employed to understand how the issue is being treated in the literature. IS Theory: Considering the societal nature of ethnicity, applications that evaluate ethnicity are subjected to Social Shaping Of The Technology. Investigating these applications can help identify the societal influence of those technologies. Methods: We present a systematic literature review (SLR) to map the algorithms, types, sources of the data, and results obtained for classifying ethnicity based on the human face. Summary of Results: Many algorithms are currently used to ascertain ethnicity from face images. These algorithms may use information from the whole face or opt to use partial regions of the face. The strategies may use symbolic data about the face, like face measurements, or use subsymbolic data. The classification performance appears to be stagnated. However, datasets overrepresent White, Black, and Asian subjects, with few datasets being balanced. Contributions and Impacts in the IS area: This study offers an up-to-date look at how ethnicity classification is done, the data used, and the way society may be shaping technology.
- Roger Achkar, GabyAbou Haidar, Mireille el Assal, Danny Habchy, Diala al Ashi, and Tarek Maylaa. 2019. Ethnicity Recognition System using Back Propagation Algorithm of an MLP. (2019), 1–5. https://doi.org/10.1109/ACTEA.2019.8851071Google Scholar
Cross Ref
- Mazida A. Ahmed, Ridip Dev Choudhury, and Kishore Kashyap. 2020. Race estimation with deep networks. Journal of King Saud University - Computer and Information Sciences (2020). https://doi.org/10.1016/j.jksuci.2020.11.029Google ScholarDigital Library
- Sadam Al-Azani and El-Sayed M. El-Alfy. 2019. Ethnicity recognition under difficult scenarios using HOG. (2019), 1–5. https://doi.org/10.1049/cp.2019.0176Google ScholarCross Ref
- Tarik Alafif, Zeyad Hailat, Melih Aslan, and Xuewen Chen. 2017. On Classifying Facial Races with Partial Occlusions and Pose Variations. (2017), 679–684. https://doi.org/10.1109/ICMLA.2017.00-82Google ScholarCross Ref
- Norah A. Al-Humaidan And and Master Prince. 2021. A Classification of Arab Ethnicity Based on Face Image Using Deep Learning Approach. IEEE Access 9.0 (2021), 50755–50766. https://doi.org/10.1109/ACCESS.2021.3069022Google ScholarCross Ref
- Chhandak Bagchi, D. Geraldine Bessie Amali, and M. Dinakaran. 2019. Accurate Facial Ethnicity Classification Using Artificial Neural Networks Trained with Galactic Swarm Optimization Algorithm. In Information Systems Design and Intelligent Applications, Suresh Chandra Satapathy, Vikrant Bhateja, Radhakhrishna Somanah, Xin-She Yang, and Roman Senkerik (Eds.). Singapore, 123–132.Google Scholar
- Talha Imtiaz Baig, Talha Mahboob Alam, Tayaba Anjum, Sheraz Naseer, Abdul Wahab, Maria Imtiaz, and Muhammad Mehdi Raza. 2019. Classification of Human Face: Asian and Non-Asian People. (2019), 1–6. https://doi.org/10.1109/ICIC48496.2019.8966721Google ScholarCross Ref
- Chris Ballentine. 1983. Who is a Negro–Revisisted: Determining Individual Racial Status for Purposes of Affirmative Action. U. Fla. L. Rev. 35 (1983), 683.Google Scholar
- Fabiola Becerra-Riera, Nelson Méndez Llanes, Annette Morales-González, Heydi Méndez-Vázquez, and Massimo Tistarelli. 2019. On Combining Face Local Appearance and Geometrical Features for Race Classification. In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, Ruben Vera-Rodriguez, Julian Fierrez, and Aythami Morales (Eds.). Cham, 567–574.Google Scholar
- David Belcar, Petra Grd, and Igor Tomičić. 2022. Automatic Ethnicity Classification from Middle Part of the Face Using Convolutional Neural Networks. Informatics 9, 1 (2022). https://doi.org/10.3390/informatics9010018Google ScholarCross Ref
- Sebastian Benthall and Bruce D. Haynes. 2019. Racial Categories in Machine Learning. In Proceedings of the Conference on Fairness, Accountability, and Transparency (Atlanta, GA, USA) (FAT* ’19). New York, NY, USA, 289–298. https://doi.org/10.1145/3287560.3287575Google ScholarDigital Library
- Elhocine Boutellaa, Abdenour Hadid, Messaoud Bengherabi, and Samy Ait-Aoudia. 2015. On the use of Kinect depth data for identity, gender and ethnicity classification from facial images. Pattern Recognition Letters 68 (2015), 270–277. https://doi.org/10.1016/j.patrec.2015.06.027 Special Issue on “Soft Biometrics”.Google ScholarDigital Library
- Paula Branco, Luís Torgo, and Rita P. Ribeiro. 2016. A Survey of Predictive Modeling on Imbalanced Domains. ACM Comput. Surv. 49, 2, Article 31 (aug 2016), 50 pages. https://doi.org/10.1145/2907070Google ScholarDigital Library
- Graham Byatt and Gillian Rhodes. 2004. Identification of own-race and other-race faces: Implications for the representation of race in face space. Psychonomic Bulletin & Review 11, 4 (2004), 735–741.Google ScholarCross Ref
- Antitza Dantcheva, Petros Elia, and Arun Ross. 2016. What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics. IEEE Transactions on Information Forensics and Security 11, 3 (2016), 441–467. https://doi.org/10.1109/TIFS.2015.2480381Google ScholarDigital Library
- Sujitha Juliet Devaraj, R. Catherine Joy, I. Santhosh, and I. C. Kevin. 2021. Deep Learning Based Facial Feature Detection for Ethnicity Recognition. In Smart Computing Techniques and Applications, Suresh Chandra Satapathy, Vikrant Bhateja, Margarita N. Favorskaya, and T. Adilakshmi (Eds.). Singapore, 527–534.Google Scholar
- Huaxiong Ding, Di Huang, Yunhong Wang, and Liming Chen. 2013. Facial ethnicity classification based on boosted local texture and shape descriptions. In 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG). 1–6. https://doi.org/10.1109/FG.2013.6553815Google ScholarCross Ref
- Mingxing Duan, Kenli Li, Xiangke Liao, Keqin Li, and Qi Tian. 2019. Features-Enhanced Multi-Attribute Estimation with Convolutional Tensor Correlation Fusion Network. ACM Trans. Multimedia Comput. Commun. Appl. 15.0 (2019). https://doi.org/10.1145/3355542Google ScholarDigital Library
- Yicheng Fan, Qijun Zhao, and Daning Wang. 2020. On 3d Face Attributes Analysis Using Deep Learning: A Preliminary Case Study on Gender and Ethnicity Recognition. (2020), 69–73. https://doi.org/10.1145/3421558.3421569Google ScholarDigital Library
- Annette Flanagin, Tracy Frey, Stacy L Christiansen, and AMA Manual of Style Committee. 2021. Updated guidance on the reporting of race and ethnicity in medical and science journals. JAMA 326, 7 (Aug. 2021), 621–627.Google Scholar
- Matheus Freitas and Rayza Sarmento. 2020. As falas sobre a fraude: análise das notícias sobre casos de fraudes nas cotas raciais em universidades em Minas Gerais. Revista Brasileira de Estudos Pedagógicos 101, 258 (Aug. 2020). https://doi.org/10.24109/2176-6681.rbep.101i258.4262Google ScholarCross Ref
- Shixin Gao, Chuisheng Zeng, Mingze Bai, and Kunxian Shu. 2020. Facial Ethnicity Recognition Based on Transfer Learning from Deep Convolutional Networks. (2020), 310–314. https://doi.org/10.1109/AEMCSE50948.2020.00073Google ScholarCross Ref
- Timnit Gebru. 2019. Oxford Handbook on AI Ethics Book Chapter on Race and Gender. https://doi.org/10.48550/ARXIV.1908.06165Google ScholarCross Ref
- Asma El Kissi Ghalleb, Safa Boumaiza, and Najoua Essoukri Ben Amara. 2020. Demographic Face Profiling Based on Age, Gender and Race. (2020), 1–6. https://doi.org/10.1109/ATSIP49331.2020.9231835Google ScholarCross Ref
- Ramán Grosfoguel. 2004. Race and Ethnicity or Racialized Ethnicities?: Identities within Global Coloniality. Ethnicities 4, 3 (2004), 315–336. https://doi.org/10.1177/1468796804045237 arXiv:https://doi.org/10.1177/1468796804045237Google ScholarCross Ref
- Bingchen H. Guo, Mark S. Nixon, and John N. Carter. 2019. Soft Biometric Fusion for Subject Recognition at a Distance. IEEE Transactions on Biometrics, Behavior, and Identity Science 1, 4 (2019), 292–301. https://doi.org/10.1109/TBIOM.2019.2943934Google ScholarCross Ref
- YiJun Guo, Guljamal Ubul, Nurbiya Yadikar, Mutallip Mamut, and Kurban Ubul. 2020. A Survey of Multi-Ethnic Face Feature Recognition. In Proceedings of the 2020 9th International Conference on Computing and Pattern Recognition (Xiamen, China) (ICCPR 2020). New York, NY, USA, 168–173. https://doi.org/10.1145/3436369.3437412Google ScholarDigital Library
- Rishi Gupta, Sandeep Kumar, Pradeep Yadav, and Sumit Shrivastava. 2018. Identification of Age, Gender, & Race SMT (Scare, Marks, Tattoos) from Unconstrained Facial Images Using Statistical Techniques. In 2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE). 1–8. https://doi.org/10.1109/ICSCEE.2018.8538423Google ScholarCross Ref
- S. Gutta, H. Wechsler, and P.J. Phillips. 1998. Gender and ethnic classification of face images. In Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition. 194–199. https://doi.org/10.1109/AFGR.1998.670948Google ScholarCross Ref
- Skander Hamdi and Abdelouahab Moussaoui. 2020. Comparative study between machine and deep learning methods for age, gender and ethnicity identification. (2020), 1–6. https://doi.org/10.1109/ISIA51297.2020.9416549Google ScholarCross Ref
- Bilal Hassan, Ebroul Izquierdo, and Tomas Piatrik. 2021. Soft biometrics: a survey. Multimedia Tools and Applications (March 2021). https://doi.org/10.1007/s11042-021-10622-8Google ScholarCross Ref
- Zhao Heng, Manandhar Dipu, and Kim-Hui Yap. 2018. Hybrid Supervised Deep Learning for Ethnicity Classification using Face Images. (2018), 1–5. https://doi.org/10.1109/ISCAS.2018.8351370Google ScholarCross Ref
- S. Hosoi, E. Takikawa, and M. Kawade. 2004. Ethnicity estimation with facial images. In Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings.195–200. https://doi.org/10.1109/AFGR.2004.1301530Google ScholarCross Ref
- Changhun Hyun and Hyeyoung Park. 2017. Recognition of Facial Attributes Using Multi-Task Learning of Deep Networks. (2017), 284–288. https://doi.org/10.1145/3055635.3056618Google ScholarDigital Library
- Rachael E. Jack and Philippe G. Schyns. 2015. The Human Face as a Dynamic Tool for Social Communication. Current Biology 25, 14 (2015), R621–R634. https://doi.org/10.1016/j.cub.2015.05.052Google ScholarCross Ref
- Md. Jewel, Md. Ismail Hossain, and Tamanna Haider Tonni. 2019. Bengali Ethnicity Recognition and Gender Classification using CNN amp; Transfer Learning. (2019), 390–396. https://doi.org/10.1109/SMART46866.2019.9117549Google ScholarCross Ref
- Shelina Khalid Jilani, Hassan Ugail, Ali Maina Bukar, and Andrew Logan. 2019. On the Ethnic Classification of Pakistani Face using Deep Learning. (2019), 191–198. https://doi.org/10.1109/CW.2019.00039Google ScholarCross Ref
- Shelina Khalid Jilani, Hassan Ugail, Ali M. Bukar, Andrew Logan, and Tasnim Munshi. 2017. A Machine Learning Approach for Ethnic Classification: The British Pakistani Face. In 2017 International Conference on Cyberworlds (CW). 170–173. https://doi.org/10.1109/CW.2017.27Google ScholarCross Ref
- Shelina Khalid Jilani, Hassan Ugail, Ali M. Bukar, Andrew Logan, and Tasnim Munshi. 2017. A Machine Learning Approach for Ethnic Classification: The British Pakistani Face. (2017), 170–173. https://doi.org/10.1109/CW.2017.27Google ScholarCross Ref
- Ayşe Kale and Oğuz Altun. 2021. Age, Gender and Ethnicity Classification from Face Images with CNN-Based Features. In 2021 Innovations in Intelligent Systems and Applications Conference (ASYU). 1–6. https://doi.org/10.1109/ASYU52992.2021.9598986Google ScholarCross Ref
- Ayşe Kale and Oğuz Altun. 2021. Age, Gender and Ethnicity Classification from Face Images with CNN-Based Features. (2021), 1–6. https://doi.org/10.1109/ASYU52992.2021.9598986Google ScholarCross Ref
- Sasan Karamizadeh and Shahidan M. Abdullah. 2018. Race classification using gaussian-based weight K-nn algorithm for face recognition. The Journal of Engineering Research 6 (2018).Google Scholar
- Akbir Khan and Marwa Mahmoud. 2019. Considering Race a Problem of Transfer Learning. (2019), 100–106. https://doi.org/10.1109/WACVW.2019.00022Google ScholarCross Ref
- Khalil Khan, Jehad Ali, Irfan Uddin, Sahib Khan, and Byeong-hee Roh. 2021. A Facial Feature Discovery Framework for Race Classification Using Deep Learning. (2021). https://doi.org/10.48550/ARXIV.2104.02471Google ScholarCross Ref
- Rotem Kowner and Walter Demel. 2015. Race and Racism in Modern East Asia: Interactions, Nationalism, Gender and Lineage. Brill, Leiden, The Netherlands. https://doi.org/10.1163/9789004292932Google ScholarCross Ref
- Sarah Lempp. 2019. With the eyes of society? Doing race in affirmative action practices in Brazil. Citizenship Studies 23, 7 (2019), 703–719. https://doi.org/10.1080/13621025.2019.1651090 arXiv:https://doi.org/10.1080/13621025.2019.1651090Google ScholarCross Ref
- D Stephen Lindsay, Philip C Jack, and Marcus A Christian. 1991. Other-race face perception.Journal of applied psychology 76, 4 (1991), 587.Google Scholar
- Xiaoguang Lu and Anil K. Jain. 2004. Ethnicity identification from face images. In Biometric Technology for Human Identification(Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 5404), Anil K. Jain and Nalini K. Ratha (Eds.). 114–123. https://doi.org/10.1117/12.542847Google ScholarCross Ref
- Chenlei Lv, Zhongke Wu, Xingce Wang, Zhang Dan, and Mingquan Zhou. 2020. Ethnicity classification by the 3D Discrete Landmarks Model measure in Kendall shape space. Pattern Recognition Letters 129.0 (2020), 26–32. https://doi.org/10.1016/j.patrec.2019.10.035Google ScholarCross Ref
- Chenlei Lv, Zhongke Wu, Dan Zhang, Xingce Wang, and Mingquan Zhou. 2019. 3D Nose shape net for human gender and ethnicity classification. Pattern Recognition Letters 126.0 (2019), 51–57. https://doi.org/10.1016/j.patrec.2018.11.010 Robustness, Security and Regulation Aspects in Current Biometric Systems.Google ScholarCross Ref
- Jacek Mazurkiewicz and Arkadiusz Podziewski. 2020. Ethnicity Classification System Based on Human Face Picture. In Reliability and Statistics in Transportation and Communication, Igor Kabashkin, Irina Yatskiv, and Olegas Prentkovskis (Eds.). Cham, 430–439.Google Scholar
- Ahmad Saeed Mohammad and Jabir Alshehabi Al-Ani. 2017. Towards ethnicity detection using learning based classifiers. (2017), 219–224. https://doi.org/10.1109/CEEC.2017.8101628Google ScholarCross Ref
- Ahmad Saeed Mohammad and Jabir Alshehabi Al-Ani. 2018. Convolutional Neural Network for Ethnicity Classification using Ocular Region in Mobile Environment. (2018), 293–298. https://doi.org/10.1109/CEEC.2018.8674194Google ScholarCross Ref
- Ahmed Abdulateef Mohammed and Atul Sajjanhar. 2017. Investigation of Gender and Race Classification for Different Color Models. (2017), 1–8. https://doi.org/10.1109/DICTA.2017.8227450Google ScholarCross Ref
- Ahmed Abdulateef Mohammed and Atul Sajjanhar. 2017. Robust single-label classification of facial attributes. (2017), 651–656. https://doi.org/10.1109/ICMEW.2017.8026324Google ScholarCross Ref
- David A. Molina, Leonardo Causa, and Juan Tapia. 2020. Reduction of Bias for Gender and Ethnicity from Face Images using Automated Skin Tone Classification. (2020), 1–5.Google Scholar
- Pablo Del Moral, Sławomir Nowaczyk, and Sepideh Pashami. 2022. Why Is Multiclass Classification Hard?IEEE Access 10 (2022), 80448–80462. https://doi.org/10.1109/ACCESS.2022.3192514Google ScholarCross Ref
- Laura Morán-Fernández, Verónica Bólon-Canedo, and Amparo Alonso-Betanzos. 2022. How important is data quality? Best classifiers vs best features. Neurocomputing 470 (2022), 365–375. https://doi.org/10.1016/j.neucom.2021.05.107Google ScholarDigital Library
- Ghulam Muhammad, Muhammad Hussain, Fatmah Alenezy, George Bebis, Anwar M. Mirza, and Hatim Aboalsamh. 2012. Race Classification From Face Images Using Local Descriptors. International Journal on Artificial Intelligence Tools 21, 05 (2012), 1250019. https://doi.org/10.1142/S0218213012500194Google ScholarCross Ref
- Wing W. Y. Ng, Zixin Zhou, and Ting Wang. 2021. Fine-Grained Facial Ethnicity Recognition Based on Dual Convolutional Autoencoders. (2021), 235–240. https://doi.org/10.1109/ICICIP53388.2021.9642208Google ScholarCross Ref
- Mark S. Nixon, Bingchen H. Guo, Sarah V. Stevenage, Emad S. Jaha, Nawaf Almudhahka, and Daniel Martinho-Corbishley. 2017. Towards automated eyewitness descriptions: describing the face, body and clothing for recognition. Visual Cognition 25, 4-6 (2017), 524–538. https://doi.org/10.1080/13506285.2016.1266426 arXiv:https://doi.org/10.1080/13506285.2016.1266426Google ScholarCross Ref
- U.S. Department of State. 2005. Marriage to Saudis. https://web.archive.org/web/20120614045804http://travel.state.gov/travel/cis_pa_tw/tw/tw_931.htmlGoogle Scholar
- E O Omidiora, O. Ojo, N.A. Yekini, and T.O. Tubi. 2012. Analysis, Design and Implementation of Human Fingerprint Patterns System “Towards Age & Gender Determination, Ridge Thickness To Valley Thickness Ratio (RTVTR) & Ridge Count On Gender Detection. International Journal of Advanced Research in Artificial Intelligence 1, 2 (2012). https://doi.org/10.14569/IJARAI.2012.010210Google ScholarCross Ref
- Mohd Zamri Osman, Mohd Aizaini Maarof, Mohd Foad Rohani, Nilam Nur Amir Sjarif, and Nor Saradatul Akmar Zulkifli. 2020. A multi-color based features from facial images for automatic ethnicity identification model. Indonesian Journal of Electrical Engineering and Computer Science 18, 3 (June 2020), 1383. https://doi.org/10.11591/ijeecs.v18.i3.pp1383-1390Google ScholarCross Ref
- Alice J. O’Toole and Vaidehi Natu. 2013. Computational perspectives on the other-race effect. Visual Cognition 21, 9-10 (2013), 1121–1137. https://doi.org/10.1080/13506285.2013.803505 arXiv:https://doi.org/10.1080/13506285.2013.803505Google ScholarCross Ref
- Burcu Ozcelik. 2021. Introduction: confronting the legacy and contemporary iterations of racial politics in the Middle East. Ethnic and Racial Studies 44, 12 (2021), 2155–2166. https://doi.org/10.1080/01419870.2021.1919312 arXiv:https://doi.org/10.1080/01419870.2021.1919312Google ScholarCross Ref
- Matthew J Page, Joanne E McKenzie, Patrick M Bossuyt, Isabelle Boutron, Tammy C Hoffmann, Cynthia D Mulrow, Larissa Shamseer, Jennifer M Tetzlaff, Elie A Akl, Sue E Brennan, Roger Chou, Julie Glanville, Jeremy M Grimshaw, Asbjørn Hróbjartsson, Manoj M Lalu, Tianjing Li, Elizabeth W Loder, Evan Mayo-Wilson, Steve McDonald, Luke A McGuinness, Lesley A Stewart, James Thomas, Andrea C Tricco, Vivian A Welch, Penny Whiting, and David Moher. 2021. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372 (2021). https://doi.org/10.1136/bmj.n71 arXiv:https://www.bmj.com/content/372/bmj.n71.full.pdfGoogle ScholarCross Ref
- Irham Pratama and Felix Kurniadi. 2020. Ethnicity Classification Based On Facial Features Using Viola-Jones Algorithm. IJNMT (International Journal of New Media Technology) 7, 1 (Jul. 2020), 39–42. https://doi.org/10.31937/ijnmt.v6i2.917Google ScholarCross Ref
- Tiani Tiara Putri, Ema Rachmawati, and Febryanti Sthevanie. 2020. Indonesian Ethnicity Recognition Based on Face Image Using Uniform Local Binary Pattern (ULBP) and Color Histogram. (2020), 1–5. https://doi.org/10.1109/ICICoS51170.2020.9299103Google ScholarCross Ref
- Dinda Mareta Putriany, Ema Rachmawati, and Febryanti Sthevanie. 2021. Indonesian Ethnicity Recognition Based on Face Image Using Gray Level Co-occurrence Matrix and Color Histogram. IOP Conference Series: Materials Science and Engineering 1077, 1 (feb 2021), 012040. https://doi.org/10.1088/1757-899X/1077/1/012040Google ScholarCross Ref
- Hafidh Fikri Rasyid, Kurniawan Nur Ramadhani, and Febryanti Sthevanie. 2018. Mongoloid and Non-Mongoloid Race Classification from Face Image Using Local Binary Pattern Feature Extractions. (2018), 329–332. https://doi.org/10.1109/ICoICT.2018.8528783Google ScholarCross Ref
- Sean F. Reardon, Rachel Baker, Matt Kasman, Daniel Klasik, and Joseph B. Townsend. 2018. What Levels of Racial Diversity Can Be Achieved with Socioeconomic-Based Affirmative Action? Evidence from a Simulation Model. Journal of Policy Analysis and Management 37, 3 (April 2018), 630–657. https://doi.org/10.1002/pam.22056Google ScholarCross Ref
- Abdul Rehman, Gulraiz Khan, Aiman Siddiqi, Abdullah Khan, and Usman Ghani Khan. 2018. Modified Texture Features from Histogram and Gray Level Co-occurence Matrix of Facial Data for Ethnicity Detection. (2018), 1–6. https://doi.org/10.1109/IMTIC.2018.8467231Google ScholarCross Ref
- Daniel Riccio, Genny Tortora, Maria De Marsico, and Harry Wechsler. 2012. EGA — Ethnicity, gender and age, a pre-annotated face database. In 2012 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications (BIOMS) Proceedings. 1–8. https://doi.org/10.1109/BIOMS.2012.6345776Google ScholarCross Ref
- S. Md. Mansoor Roomi, S.L. Virasundarii, S. Selvamegala, S. Jeevanandham, and D. Hariharasudhan. 2011. Race Classification Based on Facial Features. In 2011 Third National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics. 54–57. https://doi.org/10.1109/NCVPRIPG.2011.19Google ScholarDigital Library
- Paul Ruvolo, Jacob Whitehill, and Javier R Movellan. 2013. Exploiting commonality and interaction effects in crowdsourcing tasks using latent factor models. In Neural Information Processing Systems. Workshop on Crowdsourcing: Theory, Algorithms and Applications. Citeseer.Google Scholar
- Mezzoudj Saliha, Behloul Ali, and Seghir Rachid. 2019. Towards large-scale face-based race classification on spark framework. Multimedia Tools and Applications 78, 18 (June 2019), 26729–26746. https://doi.org/10.1007/s11042-019-7672-7Google ScholarDigital Library
- Amer A. Sallam, Muhammad Nomani Kabir, Athmar N. M. Shamhan, Heba K. Nasser, and Jing Wang. 2021. A Racial Recognition Method Based on Facial Color and Texture for Improving Demographic Classification. In Proceedings of the 11th National Technical Seminar on Unmanned System Technology 2019, Zainah Md Zain, Hamzah Ahmad, Dwi Pebrianti, Mahfuzah Mustafa, Nor Rul Hasma Abdullah, Rosdiyana Samad, and Maziyah Mat Noh (Eds.). Singapore, 843–852.Google ScholarCross Ref
- Thorsten Schoormann, Dennis Behrens, Michael Fellmann, and Ralf Knackstedt. 2021. On Your Mark, Ready, Search: A Framework for Structuring Literature Search Strategies in Information Systems. 558–575. https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-030-86790-4_38Google Scholar
- Sefik Ilkin Serengil and Alper Ozpinar. 2021. HyperExtended LightFace: A Facial Attribute Analysis Framework. (2021), 1–4. https://doi.org/10.1109/ICEET53442.2021.9659697Google ScholarCross Ref
- G. Shakhnarovich, P.A. Viola, and B. Moghaddam. 2002. A unified learning framework for real time face detection and classification. In Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition. 16–23. https://doi.org/10.1109/AFGR.2002.1004124Google ScholarCross Ref
- Maneet Singh, Shruti Nagpal, Mayank Vatsa, Richa Singh, Afzel Noore, and Angshul Majumdar. 2017. Gender and ethnicity classification of Iris images using deep class-encoder. (2017), 666–673. https://doi.org/10.1109/BTAS.2017.8272755Google ScholarDigital Library
- Chris Smaje. 1997. Not just a Social Construct: Theorising Race and Ethnicity. Sociology 31, 2 (1997), 307–327. https://doi.org/10.1177/0038038597031002007 arXiv:https://doi.org/10.1177/0038038597031002007Google ScholarCross Ref
- Nisha Srinivas, Harleen Atwal, Derek C. Rose, Gayathri Mahalingam, Karl Ricanek, and David S. Bolme. 2017. Age, Gender, and Fine-Grained Ethnicity Prediction Using Convolutional Neural Networks for the East Asian Face Dataset. (2017), 953–960. https://doi.org/10.1109/FG.2017.118Google ScholarDigital Library
- Stephen Steinberg. 2018. Occupational apartheid in America: Race, labor market segmentation, and affirmative action. In Without justice for all. Routledge, 215–233.Google Scholar
- Gurram Sunitha, K. Geetha, S. Neelakandan, Aditya Kumar Singh Pundir, S. Hemalatha, and Vinay Kumar. 2022. Intelligent deep learning based ethnicity recognition and classification using facial images. Image and Vision Computing 121.0 (2022), 104404. https://doi.org/10.1016/j.imavis.2022.104404Google ScholarCross Ref
- Muhammed Talo, Betul Ay, Semiha Makinist, and Galip Aydin. 2018. Bigailab-4race-50K: Race Classification with a New Benchmark Dataset. (2018), 1–4. https://doi.org/10.1109/IDAP.2018.8620759Google ScholarCross Ref
- Edward Telles. 2022. Studies on Racial Classification in Latin America. In Routledge Handbook of Afro-Latin American Studies. Routledge, 155–165. https://doi.org/10.4324/9781003159247-17Google ScholarCross Ref
- Takuma Terada, Ryusuke Kimura, and Yen–Wei Chen. 2022. Three-Dimensional Facial Ethnicity Identification Based on Cylindrical Projection and Deep Learning. (2022), 01–04. https://doi.org/10.1109/ICCE53296.2022.9730761Google ScholarCross Ref
- Muhammad Umair and Muhammad Naqeeb Nazir. 2021. Classification of Demographic Attributes from Facial Image by using CNN. (2021), 68–73. https://doi.org/10.1109/ICAI52203.2021.9445248Google ScholarCross Ref
- Cunrui Wang, Qingling Zhang, Wanquan Liu, Yu Liu, and Lixin Miao. 2019. Expression of Concern: Facial feature discovery for ethnicity recognition. WIREs Data Mining and Knowledge Discovery 9, 1 (2019), e1278. https://doi.org/10.1002/widm.1278 arXiv:https://wires.onlinelibrary.wiley.com/doi/pdf/10.1002/widm.1278Google ScholarCross Ref
- Pingyu Wang, Fei Su, and Zhicheng Zhao. 2017. Joint multi-feature fusion and attribute relationships for facial attribute prediction. (2017), 1–4. https://doi.org/10.1109/VCIP.2017.8305036Google ScholarCross Ref
- Natasha Warikoo and Utaukwa Allen. 2020. A solution to multiple problems: the origins of affirmative action in higher education around the world. Studies in Higher Education 45, 12 (2020), 2398–2412. https://doi.org/10.1080/03075079.2019.1612352 arXiv:https://doi.org/10.1080/03075079.2019.1612352Google ScholarCross Ref
- Robin Williams and David Edge. 1996. The Social Shaping of Technology. Research Policy 25 (09 1996), 865–899. https://doi.org/10.1016/0048-7333(96)00885-2Google ScholarCross Ref
- Xulei Wu, Peijiang Yuan, Tianmiao Wang, Doudou Gao, and Ying Cai. 2018. Race Classification from Face using Deep Convolutional Neural Networks. (2018), 1–6. https://doi.org/10.1109/ICARM.2018.8610704Google ScholarCross Ref
- Baiqiang Xia, Boulbaba Ben Amor, and Mohamed Daoudi. 2017. Joint gender, ethnicity and age estimation from 3D faces: An experimental illustration of their correlations. Image and Vision Computing 64.0 (2017), 90–102. https://doi.org/10.1016/j.imavis.2017.06.004Google ScholarCross Ref
- Yiting Xie, Khoa Luu, and Marios Savvides. 2012. A robust approach to facial ethnicity classification on large scale face databases. In 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS). 143–149. https://doi.org/10.1109/BTAS.2012.6374569Google ScholarCross Ref
- Lu Xu, Heng Fan, and Jinhai Xiang. 2019. Hierarchical Multi-Task Network For Race, Gender and Facial Attractiveness Recognition. (2019), 3861–3865. https://doi.org/10.1109/ICIP.2019.8803614Google ScholarCross Ref
- Tseming Yang. 2005. Choice and fraud in racial identification: The dilemma of policing race in affirmative action, the Census, and a color-blind society. Mich. J. Race & L. 11 (2005), 367.Google Scholar
- Xin Zhou, Yuqin Jin, He Zhang, Shanshan Li, and Xin Huang. 2016. A Map of Threats to Validity of Systematic Literature Reviews in Software Engineering. In 2016 23rd Asia-Pacific Software Engineering Conference (APSEC). https://doi.org/10.1109/apsec.2016.031Google ScholarCross Ref
Index Terms
- Machine-based Stereotypes: How Machine Learning Algorithms Evaluate Ethnicity from Face Data
Recommendations
Exploring Why Underrepresented Students Are Less Likely to Study Machine Learning and Artificial Intelligence
ITiCSE '21: Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education V. 1There is little research on why underrepresented minorities are less likely to specifically study Machine Learning and Artificial Intelligence (ML/AI). We surveyed 159 undergraduate students about their interest in, exposure to, and personal views on ML/...
Multimodal 2D and 3D Facial Ethnicity Classification
ICIG '09: Proceedings of the 2009 Fifth International Conference on Image and GraphicsEthnicity is an important demographic attribute of human beings, and automatic face-based classification of ethnicity has promising applications in various fields. In this paper, we explore the ethnicity discriminability of both 2D and 3D face features, ...
Racial categories in machine learning
FAT* '19: Proceedings of the Conference on Fairness, Accountability, and TransparencyControversies around race and machine learning have sparked debate among computer scientists over how to design machine learning systems that guarantee fairness. These debates rarely engage with how racial identity is embedded in our social experience, ...
Comments