Influence of Minkowski Distance Order on Deep Embedding-Based Cross-Spectral Cross-Distance Face Recognition
Keywords:
Face recognition; cross-spectral; cross-distance; Minkowski distance norm; deep embedding.
Abstract
Cross-spectral cross-distance face recognition (CSCD-FR) is a challenging biometric task involving visible (VIS) and near-infrared (NIR) facial images acquired at varying stand-off distances. Beyond the spectral discrepancy between VIS and NIR images, increasing acquisition distance degrades resolution, blurs images, and reduces facial detail, making recognition considerably more difficult. Although deep convolutional neural networks (CNNs) have demonstrated strong capabilities for learning discriminative facial embeddings, existing studies have predominantly focused on network architectures, feature representations, and learning strategies, while the influence of distance metric selection remains relatively underexplored. Since recognition decisions are ultimately based on distances between embedding vectors, the choice of similarity metric may affect recognition performance. This study therefore investigates the influence of generalized Minkowski distance order on CSCD-FR using deep embeddings extracted from MobileNetV3-Small, EfficientNet-B2, and DenseNet121. Using the Long-Distance Heterogeneous Face Database (LDHF-DB) at stand-off distances of 1, 60, and 100 m, both ImageNet-pretrained and domain-adapted models were evaluated using -normalized embeddings. Domain adaptation optimized the embedding layer using triplet margin loss while the CNN backbones remained frozen. Minkowski orders from to were evaluated using closed-set identification and verification protocols. ImageNet-pretrained models yielded Rank-1 accuracies below 11%, whereas domain adaptation substantially improved recognition performance. DenseNet121 achieved the highest observed performance, with 93.19% Rank-1 accuracy and 2.16% EER at . The Rank-1-optimal Minkowski distance order varied across architectures, with EfficientNet-B2 and MobileNetV3-Small achieving their highest observed Rank-1 accuracies at and , respectively. These findings show that selecting a similarity metric can provide an additional matching-stage optimization without modifying the CNN backbone. Because the same retained identities were used for domain adaptation and evaluation, the results represent a dataset-specific assessment rather than recognition performance on previously unseen subjects.
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References
[1] Y. Kortli, M. Jridi, A. Al Falou, and M. Atri, “Face recognition systems: a survey,” Sensors, vol. 20, no. 2, p. 342, Jan. 2020, doi: 10.3390/s20020342
[2] J. Soldera and J. Scharcanski, “Face recognition, modalities and applications [instrumentation and measurement systems],” IEEE Instrum. Meas. Mag., vol. 29, no. 1, pp. 13–20, Feb. 2026, doi: 10.1109/MIM.2026.11395469.
[3] S. Jain, V. Chaudhari, R. Chuadhari, T. Chavan, and P. Shahane, “A survey on face recognition techniques in machine learning,” Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol., pp. 50–66, Nov. 2022, doi: 10.32628/cseit228558.
[4] R. He, J. Cao, L. Song, Z. Sun, and T. Tan, “Cross-spectral face completion for nir-vis heterogeneous face recognition,” Feb. 10, 2019, arXiv: arXiv:1902.03565. doi: 10.48550/arXiv.1902.03565.
[5] D. Anghelone, C. Chen, A. Ross, and A. Dantcheva, “Beyond the visible: a survey on cross-spectral face recognition,” Neurocomputing, vol. 611, p. 128626, Jan. 2025, doi: 10.1016/j.neucom.2024.128626.
[6] E. D. Meutia, F. Arnia, Nasaruddin, R. Muharar, M. Oktiana, and K. Saddami, “Performance evaluation of deep feature in cross spectral face recognition,” in 2024 11th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), Aug. 2024, pp. 314–319. doi: 10.1109/ICITACEE62763.2024.10762811.
[7] R. He, J. Cao, L. Song, Z. Sun, and T. Tan, “Adversarial Cross-Spectral Face Completion for NIR-VIS Face Recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 42, no. 5, pp. 1025–1037, May 2020, doi: 10.1109/TPAMI.2019.2961900.
[8] N. D. Kalka, T. Bourlai, B. Cukic, and L. Hornak, “Cross-spectral face recognition in heterogeneous environments: a case study on matching visible to short-wave infrared imagery,” in 2011 International Joint Conference on Biometrics (IJCB), Oct. 2011, pp. 1–8. doi: 10.1109/IJCB.2011.6117586.
[9] P. Martins, J. S. Silva, and A. Bernardino, “Multispectral facial recognition in the wild,” Sensors, vol. 22, no. 11, p. 4219, Jun. 2022, doi: 10.3390/s22114219.
[10]. J. Li, “Adaptive diffusion models for overcoming data scarcity in long-distance face recognition,” Sci. Rep., vol. 16, no. 1, p. 4429, Jan. 2026, doi: 10.1038/s41598-025-34520-3.
[11] F. Arnia, M. Oktiana, K. Saddami, K. Munadi, R. Roslidar, and B. Pradhan, “Homomorphic filtering and phase-based matching for cross-spectral cross-distance face recognition,” Sensors, vol. 21, no. 13, p. 4575, Jul. 2021, doi: 10.3390/s21134575.
[12] D. Ai, Y. Wang, K. Jia, Z. Ji, and Y. Liu, “Cross-distance near-infrared face recognition,” J. Vis. Commun. Image Represent., vol. 115, p. 104691, Jan. 2026, doi: 10.1016/j.jvcir.2025.104691.
[13] J. Nandre, S. Rai, and B. R. Kanawade, “Comparative analysis of transfer learning cnn for face recognition,” in 2022 2nd International Conference on Intelligent Technologies (CONIT), Hubli, India: IEEE, Jun. 2022, pp. 1–6. doi: 10.1109/CONIT55038.2022.9847946.
[14] F. Wu, X.-Y. Jing, Y. Feng, Y. Ji, and R. Wang, “Spectrum-aware discriminative deep feature learning for multi-spectral face recognition,” Pattern Recognit., vol. 111, p. 107632, Mar. 2021, doi: 10.1016/j.patcog.2020.107632.
[15] A. George and S. Marcel, “Heterogeneous face recognition using domain invariant units,” in ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr. 2024, pp. 4780–4784. doi: 10.1109/ICASSP48485.2024.10447481.
[16] R. He, X. Wu, Z. Sun, and T. Tan, “Wasserstein CNN: learning invariant features for NIR-VIS face recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 41, no. 7, pp. 1761–1773, Jul. 2019, doi: 10.1109/TPAMI.2018.2842770.
[17] A. George and S. Marcel, “xEdgeFace: efficient cross-spectral face recognition for edge devices,” in 2025 IEEE International Joint Conference on Biometrics (IJCB), Osaka, Japan: IEEE, Sep. 2025, pp. 1–11. doi: 10.1109/IJCB65343.2025.11410838.
[18] A. Tu et al., “TransFIRA: Transfer Learning for Face Image recognizability assessment,” in 2026 IEEE 20th International Conference on Automatic Face and Gesture Recognition (FG), Kyoto, Japan: IEEE, May 2026, pp. 1–9. doi: 10.1109/FG67764.2026.11556959.
[19]. F. Wang, X. Xiang, J. Cheng, and A. L. Yuille, “NormFace: L2 hypersphere embedding for face verification,” in Proceedings of the 25th ACM international conference on Multimedia, Mountain View California USA: ACM, Oct. 2017, pp. 1041–1049. doi: 10.1145/3123266.3123359.
[20] J. Deng, J. Guo, J. Yang, N. Xue, I. Kotsia, and S. Zafeiriou, “ArcFace: additive angular margin loss for deep face recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 10, pp. 5962–5979, Oct. 2022, doi: 10.1109/TPAMI.2021.3087709.
[21] G. Qian, S. Sural, Y. Gu, and S. Pramanik, “Similarity between Euclidean and cosine angle distance for nearest neighbor queries,” in Proceedings of the 2004 ACM symposium on Applied computing, Nicosia Cyprus: ACM, Mar. 2004, pp. 1232–1237. doi: 10.1145/967900.968151.
[22] A. Howard et al., “Searching for MobileNetV3,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 1314–1324, doi: 10.1109/ICCV.2019.00140
[23] M. Tan and Q. Le, “EfficientNetV2: smaller models and faster training,” in Proceedings of the 38th International Conference on Machine Learning, PMLR, Jul. 2021, pp. 10096–10106. Accessed: Mar. 18, 2024. [Online]. Available: https://proceedings.mlr.press/v139/tan21a.html
[24] G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” n Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 4700–4708, doi: 10.1109/CVPR.2017.243
[25] Z. Cao, X. Cen, H. Zhao, and L. Pang, “Balancing heterogeneous image quality for improved cross-spectral face recognition,” Sensors, vol. 21, no. 7, p. 2322, Mar. 2021, doi: 10.3390/s21072322.
[26] M. Cho, T. Kim, I.-J. Kim, K. Lee, and S. Lee, “Relational deep feature learning for heterogeneous face recognition,” IEEE Trans. Inf. Forensics Secur., vol. 16, pp. 376–388, 2021, doi: 10.1109/TIFS.2020.3013186
[27] H. Maeng, S. Liao, D. Kang, S.-W. Lee, and A. K. Jain, “Nighttime face recognition at long distance: cross-distance and cross-spectral matching,” in Computer Vision – ACCV 2012, vol. 7725, pp. 708–721. doi: 10.1007/978-3-642-37444-9_55.
[28] M. Ghosh, T. Sarkar, D. Chokhani, and A. Dey, “face detection and extraction using Viola–Jones algorithm,” in Computational Advancement in Communication, Circuits and Systems, M. C. Bhowmik, S. Chakrabarty, and S. N. Sharan, Eds. Cham: Springer, 2021, pp. 87–95. doi: 10.1007/978-981-16-4035-3_9.
[29] K. M, G. A, G. Revathy, S. Senthilvadivu, and S. Venkateswaran, “Beyond depth: evaluating ResNet-50 and ResNet-152 performance on Cifar-10 dataset,” in 2024 International Conference on Sustainable Communication Networks and Application (ICSCNA), Dec. 2024, pp. 973–977. doi: 10.1109/ICSCNA63714.2024.10863975.
[30] C. C. Aggarwal, “Similarity and distances,” in Data Mining, Cham: Springer International Publishing, 2015, pp. 63–91. doi: 10.1007/978-3-319-14142-8_3.
[31] A. K. Jain, A. Ross, and S. Prabhakar, “An introduction to biometric recognition,” IEEE Trans. Circuits Syst. Video Technol., vol. 14, no. 1, pp. 4–20, Jan. 2004, doi: 10.1109/TCSVT.2003.818349.
[32] R. Ranjan, A. Bansal, J. Zheng;, H. Xu, J. Gleason, and B. Lu, “A fast and accurate system for face detection, identification, and verification,” IEEE Trans. Biom. Behav. Identity Sci., vol. 1, no. 2, Apr. 2019, doi: 10.1109/TBIOM.2019.2908436.
[33] L. L. Chambino, J. S. Silva, and A. Bernardino, “Multispectral facial recognition: a review,” IEEE Access, vol. 8, pp. 207871–207883, 2020, doi: 10.1109/ACCESS.2020.3037451.
[34] P. J. Phillips, Hyeonjoon Moon, S. A. Rizvi, and P. J. Rauss, “The FERET evaluation methodology for face-recognition algorithms,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 10, pp. 1090–1104, Oct. 2000, doi: 10.1109/34.879790.
[35] M. Saffar, A. Kalhor, and A. Habibnia, “A geometric approach for accelerating neural networks designed for classification problems,” Sci. Rep., vol. 14, no. 1, p. 17590, Jul. 2024, doi: 10.1038/s41598-024-68172-6.
[36] N. Zhang, “A study on the impact of face image quality on face recognition in the wild,” Jul. 05, 2023, arXiv: arXiv:2307.02679, Jul. 2023, doi: 10.48550/arXiv.2307.02679.
[37] Z. H. Abbas and S. H. Shaker, “The transfer learning models for face recognition: a survey,” in 2022 2nd International Conference on Advances in Engineering Science and Technology (AEST), Oct. 2022, pp. 764–768. doi: 10.1109/AEST55805.2022.10412930.
[38] G. Lokku, G. H. Reddy, and M. N. G. Prasad, A robust face recognition model using deep transfer metric learning built on AlexNet convolutional neural network,” in 2021 Int. Conf. Commun., Control Inf. Sci. (ICCISc), Jun. 2021, pp. 1–6, 10.1109/ICCISc52257.2021.9484935.
[39] L. L. Chambino, J. S. Silva, and A. Bernardino, “Multispectral face recognition using transfer learning with adaptation of domain specific units,” Sensors, vol. 21, no. 13, Art. no. 13, Jan. 2021, doi: 10.3390/s21134520.
[40] F. Schroff, D. Kalenichenko, and J. Philbin, “FaceNet: a unified embedding for face recognition and clustering,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2015, pp. 815–823. doi: 10.3390/s20020342
[41] X. Li, B. Chen, J. Li, S. Chen, and S. Huang, “Cosine distance loss for open-set image recognition,” Electronics, vol. 14, no. 1, p. 180, Jan. 2025, doi: 10.3390/electronics14010180.
[2] J. Soldera and J. Scharcanski, “Face recognition, modalities and applications [instrumentation and measurement systems],” IEEE Instrum. Meas. Mag., vol. 29, no. 1, pp. 13–20, Feb. 2026, doi: 10.1109/MIM.2026.11395469.
[3] S. Jain, V. Chaudhari, R. Chuadhari, T. Chavan, and P. Shahane, “A survey on face recognition techniques in machine learning,” Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol., pp. 50–66, Nov. 2022, doi: 10.32628/cseit228558.
[4] R. He, J. Cao, L. Song, Z. Sun, and T. Tan, “Cross-spectral face completion for nir-vis heterogeneous face recognition,” Feb. 10, 2019, arXiv: arXiv:1902.03565. doi: 10.48550/arXiv.1902.03565.
[5] D. Anghelone, C. Chen, A. Ross, and A. Dantcheva, “Beyond the visible: a survey on cross-spectral face recognition,” Neurocomputing, vol. 611, p. 128626, Jan. 2025, doi: 10.1016/j.neucom.2024.128626.
[6] E. D. Meutia, F. Arnia, Nasaruddin, R. Muharar, M. Oktiana, and K. Saddami, “Performance evaluation of deep feature in cross spectral face recognition,” in 2024 11th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), Aug. 2024, pp. 314–319. doi: 10.1109/ICITACEE62763.2024.10762811.
[7] R. He, J. Cao, L. Song, Z. Sun, and T. Tan, “Adversarial Cross-Spectral Face Completion for NIR-VIS Face Recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 42, no. 5, pp. 1025–1037, May 2020, doi: 10.1109/TPAMI.2019.2961900.
[8] N. D. Kalka, T. Bourlai, B. Cukic, and L. Hornak, “Cross-spectral face recognition in heterogeneous environments: a case study on matching visible to short-wave infrared imagery,” in 2011 International Joint Conference on Biometrics (IJCB), Oct. 2011, pp. 1–8. doi: 10.1109/IJCB.2011.6117586.
[9] P. Martins, J. S. Silva, and A. Bernardino, “Multispectral facial recognition in the wild,” Sensors, vol. 22, no. 11, p. 4219, Jun. 2022, doi: 10.3390/s22114219.
[10]. J. Li, “Adaptive diffusion models for overcoming data scarcity in long-distance face recognition,” Sci. Rep., vol. 16, no. 1, p. 4429, Jan. 2026, doi: 10.1038/s41598-025-34520-3.
[11] F. Arnia, M. Oktiana, K. Saddami, K. Munadi, R. Roslidar, and B. Pradhan, “Homomorphic filtering and phase-based matching for cross-spectral cross-distance face recognition,” Sensors, vol. 21, no. 13, p. 4575, Jul. 2021, doi: 10.3390/s21134575.
[12] D. Ai, Y. Wang, K. Jia, Z. Ji, and Y. Liu, “Cross-distance near-infrared face recognition,” J. Vis. Commun. Image Represent., vol. 115, p. 104691, Jan. 2026, doi: 10.1016/j.jvcir.2025.104691.
[13] J. Nandre, S. Rai, and B. R. Kanawade, “Comparative analysis of transfer learning cnn for face recognition,” in 2022 2nd International Conference on Intelligent Technologies (CONIT), Hubli, India: IEEE, Jun. 2022, pp. 1–6. doi: 10.1109/CONIT55038.2022.9847946.
[14] F. Wu, X.-Y. Jing, Y. Feng, Y. Ji, and R. Wang, “Spectrum-aware discriminative deep feature learning for multi-spectral face recognition,” Pattern Recognit., vol. 111, p. 107632, Mar. 2021, doi: 10.1016/j.patcog.2020.107632.
[15] A. George and S. Marcel, “Heterogeneous face recognition using domain invariant units,” in ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr. 2024, pp. 4780–4784. doi: 10.1109/ICASSP48485.2024.10447481.
[16] R. He, X. Wu, Z. Sun, and T. Tan, “Wasserstein CNN: learning invariant features for NIR-VIS face recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 41, no. 7, pp. 1761–1773, Jul. 2019, doi: 10.1109/TPAMI.2018.2842770.
[17] A. George and S. Marcel, “xEdgeFace: efficient cross-spectral face recognition for edge devices,” in 2025 IEEE International Joint Conference on Biometrics (IJCB), Osaka, Japan: IEEE, Sep. 2025, pp. 1–11. doi: 10.1109/IJCB65343.2025.11410838.
[18] A. Tu et al., “TransFIRA: Transfer Learning for Face Image recognizability assessment,” in 2026 IEEE 20th International Conference on Automatic Face and Gesture Recognition (FG), Kyoto, Japan: IEEE, May 2026, pp. 1–9. doi: 10.1109/FG67764.2026.11556959.
[19]. F. Wang, X. Xiang, J. Cheng, and A. L. Yuille, “NormFace: L2 hypersphere embedding for face verification,” in Proceedings of the 25th ACM international conference on Multimedia, Mountain View California USA: ACM, Oct. 2017, pp. 1041–1049. doi: 10.1145/3123266.3123359.
[20] J. Deng, J. Guo, J. Yang, N. Xue, I. Kotsia, and S. Zafeiriou, “ArcFace: additive angular margin loss for deep face recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 10, pp. 5962–5979, Oct. 2022, doi: 10.1109/TPAMI.2021.3087709.
[21] G. Qian, S. Sural, Y. Gu, and S. Pramanik, “Similarity between Euclidean and cosine angle distance for nearest neighbor queries,” in Proceedings of the 2004 ACM symposium on Applied computing, Nicosia Cyprus: ACM, Mar. 2004, pp. 1232–1237. doi: 10.1145/967900.968151.
[22] A. Howard et al., “Searching for MobileNetV3,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 1314–1324, doi: 10.1109/ICCV.2019.00140
[23] M. Tan and Q. Le, “EfficientNetV2: smaller models and faster training,” in Proceedings of the 38th International Conference on Machine Learning, PMLR, Jul. 2021, pp. 10096–10106. Accessed: Mar. 18, 2024. [Online]. Available: https://proceedings.mlr.press/v139/tan21a.html
[24] G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” n Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 4700–4708, doi: 10.1109/CVPR.2017.243
[25] Z. Cao, X. Cen, H. Zhao, and L. Pang, “Balancing heterogeneous image quality for improved cross-spectral face recognition,” Sensors, vol. 21, no. 7, p. 2322, Mar. 2021, doi: 10.3390/s21072322.
[26] M. Cho, T. Kim, I.-J. Kim, K. Lee, and S. Lee, “Relational deep feature learning for heterogeneous face recognition,” IEEE Trans. Inf. Forensics Secur., vol. 16, pp. 376–388, 2021, doi: 10.1109/TIFS.2020.3013186
[27] H. Maeng, S. Liao, D. Kang, S.-W. Lee, and A. K. Jain, “Nighttime face recognition at long distance: cross-distance and cross-spectral matching,” in Computer Vision – ACCV 2012, vol. 7725, pp. 708–721. doi: 10.1007/978-3-642-37444-9_55.
[28] M. Ghosh, T. Sarkar, D. Chokhani, and A. Dey, “face detection and extraction using Viola–Jones algorithm,” in Computational Advancement in Communication, Circuits and Systems, M. C. Bhowmik, S. Chakrabarty, and S. N. Sharan, Eds. Cham: Springer, 2021, pp. 87–95. doi: 10.1007/978-981-16-4035-3_9.
[29] K. M, G. A, G. Revathy, S. Senthilvadivu, and S. Venkateswaran, “Beyond depth: evaluating ResNet-50 and ResNet-152 performance on Cifar-10 dataset,” in 2024 International Conference on Sustainable Communication Networks and Application (ICSCNA), Dec. 2024, pp. 973–977. doi: 10.1109/ICSCNA63714.2024.10863975.
[30] C. C. Aggarwal, “Similarity and distances,” in Data Mining, Cham: Springer International Publishing, 2015, pp. 63–91. doi: 10.1007/978-3-319-14142-8_3.
[31] A. K. Jain, A. Ross, and S. Prabhakar, “An introduction to biometric recognition,” IEEE Trans. Circuits Syst. Video Technol., vol. 14, no. 1, pp. 4–20, Jan. 2004, doi: 10.1109/TCSVT.2003.818349.
[32] R. Ranjan, A. Bansal, J. Zheng;, H. Xu, J. Gleason, and B. Lu, “A fast and accurate system for face detection, identification, and verification,” IEEE Trans. Biom. Behav. Identity Sci., vol. 1, no. 2, Apr. 2019, doi: 10.1109/TBIOM.2019.2908436.
[33] L. L. Chambino, J. S. Silva, and A. Bernardino, “Multispectral facial recognition: a review,” IEEE Access, vol. 8, pp. 207871–207883, 2020, doi: 10.1109/ACCESS.2020.3037451.
[34] P. J. Phillips, Hyeonjoon Moon, S. A. Rizvi, and P. J. Rauss, “The FERET evaluation methodology for face-recognition algorithms,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 10, pp. 1090–1104, Oct. 2000, doi: 10.1109/34.879790.
[35] M. Saffar, A. Kalhor, and A. Habibnia, “A geometric approach for accelerating neural networks designed for classification problems,” Sci. Rep., vol. 14, no. 1, p. 17590, Jul. 2024, doi: 10.1038/s41598-024-68172-6.
[36] N. Zhang, “A study on the impact of face image quality on face recognition in the wild,” Jul. 05, 2023, arXiv: arXiv:2307.02679, Jul. 2023, doi: 10.48550/arXiv.2307.02679.
[37] Z. H. Abbas and S. H. Shaker, “The transfer learning models for face recognition: a survey,” in 2022 2nd International Conference on Advances in Engineering Science and Technology (AEST), Oct. 2022, pp. 764–768. doi: 10.1109/AEST55805.2022.10412930.
[38] G. Lokku, G. H. Reddy, and M. N. G. Prasad, A robust face recognition model using deep transfer metric learning built on AlexNet convolutional neural network,” in 2021 Int. Conf. Commun., Control Inf. Sci. (ICCISc), Jun. 2021, pp. 1–6, 10.1109/ICCISc52257.2021.9484935.
[39] L. L. Chambino, J. S. Silva, and A. Bernardino, “Multispectral face recognition using transfer learning with adaptation of domain specific units,” Sensors, vol. 21, no. 13, Art. no. 13, Jan. 2021, doi: 10.3390/s21134520.
[40] F. Schroff, D. Kalenichenko, and J. Philbin, “FaceNet: a unified embedding for face recognition and clustering,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2015, pp. 815–823. doi: 10.3390/s20020342
[41] X. Li, B. Chen, J. Li, S. Chen, and S. Huang, “Cosine distance loss for open-set image recognition,” Electronics, vol. 14, no. 1, p. 180, Jan. 2025, doi: 10.3390/electronics14010180.
Published
2026-10-30
How to Cite
[1]
E. D. Meutia, N. Nasaruddin, R. Muharar, and F. Arnia, “Influence of Minkowski Distance Order on Deep Embedding-Based Cross-Spectral Cross-Distance Face Recognition ”, j.electron.electromedical.eng.med.inform, vol. 8, no. 4, pp. 1753-1768, Oct. 2026.
Section
Electronics
Copyright (c) 2026 Ernita Dewi Meutia, Nasaruddin Nasaruddin, Rusdha Muharar, Fitri Arnia
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