Cited by 76Face recognition and analysisExplainable Artificial Intelligence (XAI)Biometric Identification and Security

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Explainable Face Recognition

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Williford, Jonathan R., May, Brandon B., Byrne, Jeffrey·2020·Lecture Notes in Computer Science

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Research works printed but not linked: 36

  1. Bau, D., Zhou, B., Khosla, A., Oliva, A., Torralba, A.: Network dissection: quantifying interpretability of deep visual representations. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3319–3327 (2017) · DOI · Paper page
  2. Buolamwini, J., Gebru, T.: Gender shades: intersectional accuracy disparities in commercial gender classification. In: Friedler, S.A., Wilson, C., (eds) Proceedings of the 1st Conference on Fairness, Accountability and Transparency, Proceedings of Machine Learning Research, New York, NY, USA, 23–24 February 2018, vol. 81, pp. 77–91. PMLR (2018)
  3. Cao, C., et al.: Look and think twice: capturing top-down visual attention with feedback convolutional neural networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2956–2964 (2015) · DOI
  4. Castanon, G., Byrne., J.: Visualizing and quantifying discriminative features for face recognition. In: International Conference on Automatic Face and Gesture Recognition (2018) · DOI
  5. Crispell, D., Bazik, M.: Pix2Face: direct 3D face model estimation. In: 2017 IEEE International Conference on Computer Vision Workshop (ICCVW), pp. 2512–2518, October 2017 · DOI
  6. Dabkowski, P., Gal, Y.: Real time image saliency for black box classifiers. In: Advances in Neural Information Processing Systems, pp. 6967–6976 (2017)
  7. Dhar, P., Bansal, A., Castillo, C.D., Gleason, J., Phillips, P.J., Chellappa, R.: How are attributes expressed in face DCNNs? ArXiv, abs/1910.05657 (2019) · arXiv
  8. Duchaine, B., Nakayama, K.: The Cambridge face memory test: results for neurologically intact individuals and an investigation of its validity using inverted face stimuli and prosopagnosic participants. Neuropsychologia 44, 576–585 (2006) · DOI
  9. Fong, R., Vedaldi, A.: Interpretable Explanations of Black Boxes by Meaningful Perturbation. arXiv preprint (2017) · DOI · Paper page
  10. Grimm, C., Arumugam, D., Karamcheti, S., Abel, D., Wong, L.L., Littman, M.L.: Latent attention networks. arXiv:1706.00536v1 (2017) · arXiv
  11. Grother, P., Ngan, M., Hanaoka, K.: Face recognition vendor test (FRVT) Part 3: demographic effects. In: NISTIR 8280 (2019) · DOI
  12. Hu, R., Andreas, J., Darrell, T., Saenko, K.: Explainable neural computation via stack neural module networks. In: ECCV (2018) · DOI
  13. Kindermans, P.-J., et al.: Learning how to explain neural networks: patternnet and pattern attribution. arXiv preprint arXiv:1705.05598 (2017) · arXiv
  14. Li, H., Mueller, K., Chen, X.: Beyond saliency: understanding convolutional neural networks from saliency prediction on layer-wise relevance propagation. Image Vis. Comput. 83–84, 70–86 (2017)
  15. Mahendran, A., Vedaldi, A.: Understanding deep image representations by inverting them. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015) · DOI · Paper page
  16. Maze, B., et al.: IARPA Janus benchmark-c: face dataset and protocol. In: 2018 International Conference on Biometrics (ICB), pp. 158–165. IEEE (2018) · DOI
  17. Nguyen, A.M., Dosovitskiy, A., Yosinski, J., Brox, T., Clune, J.: Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. In: NIPS (2016)
  18. Parkhi, O., Vedaldi, A., Zisserman, A.: Deep face recognition. In: BMVC (2015) · DOI · Paper page
  19. Petsiuk, V., Das, A., Saenko, K.: Rise: randomized input sampling for explanation of black-box models. In: British Machine Vision Conference (BMVC) (2018)
  20. Phillips, P.J., et al.: Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms. In: Proceedings of the National Academy of Sciences of the United States of America (2018) · DOI · Paper page
  21. Raji, I.D., Buolamwini, J.: Actionable auditing: investigating the impact of publicly naming biased performance results of commercial AI products. In: AIES 2019 (2019) · DOI · Paper page
  22. Ribeiro, M.T., Singh, S., Guestrin, C.: Why should i trust you?: Explaining the predictions of any classifier. In: KDD 2016 (2016) · DOI · Paper page
  23. RichardWebster, B., Kwon, S.Y., Clarizio, C., Anthony, S.E., Scheirer, W.J.: Visual psychophysics for making face recognition algorithms more explainable. In: European Conference on Computer Vision (ECCV) (2018) · DOI
  24. Samek, W., Montavon, G., Vedaldi, A., Hansen, L.K., Müller, K. (eds.): Explaining and Visualizing Deep Learning Explainable AI: Interpreting. Springer, Heidelberg (2019). https://doi.org/10.1007/978-3-030-28954-6 · DOI · Paper page
  25. Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In CVPR (2015) · DOI · Paper page
  26. Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 618–626 (2016) · DOI · Paper page
  27. Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps. In: ICLR, p. 1 (2014)
  28. Stylianou, A., Souvenir, R., Pless, R.: Visualizing deep similarity networks. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 2029–2037. IEEE (2019) · DOI
  29. Wu, X., He, R., Sun, Z., Tan, T.: A light CNN for deep face representation with noisy labels. IEEE Trans. Inf. Forensics Secur. 13(11), 2884–2896 (2018) · DOI
  30. Xu, T., et al.: Deeper interpretability of deep networks. ArXiv, abs/1811.07807 (2018) · arXiv
  31. Yin, B., Tran, L., Li, H., Shen, X., Liu, X.: Towards interpretable face recognition. In: Proceeding of International Conference on Computer Vision, Seoul, South Korea, October 2019 · DOI · Paper page
  32. Zee, T., Gali, G., Nwogu, I.: Enhancing human face recognition with an interpretable neural network. In: The IEEE International Conference on Computer Vision (ICCV) Workshops, October 2019 · DOI
  33. Zhang, J., Lin, Z., Brandt, J., Shen, X., Sclaroff, S.: Top-down neural attention by excitation backprop. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9908, pp. 543–559 (2016) · DOI
  34. Zheng, C., Cham, T.-J., Cai, J.: Pluralistic image completion. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1438–1447 (2019) · DOI
  35. Zhong, Y., Deng, W.: Exploring features and attributes in deep face recognition using visualization techniques. In: IEEE International Conference on Automatic Face and Gesture Recognition (FG 2019) (2019) · DOI
  36. Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: CVPR (2016) · DOI · Paper page

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Metadata

Authors
Williford, J. R., May, B. B., & Byrne, J.
Affiliations
Systems and Technology Research (United States) (US)
Corresponding author
May, Brandon B.
Venue
Lecture Notes in Computer Science
Published
2020
Pages
248-263
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
OpenAlex
W3047300400
Cited by
76
References
4
Citations by year
2026: 7 · 2025: 5 · 2024: 13 · 2023: 15 · 2022: 29 · 2021: 7
FWCI
3.93
Type
conference paper
Language
en
Open access
Locations
  • Lecture notes in computer science (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
Keywords
face matching, attention maps, benchmark

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