[1] Zhao W, Chellappa R, Phillips PJ, Rosenfeld A (2003) Face recognition: a literature survey. ACM Comput Surv 35(4):399–458

[2] Parmar DN, Mehta BB (2014) Face recognition methods and applications. 4(1):84–86. arXiv:1403.0485

[3] Hassaballah M, Aly S (2015) Face recognition: challenges, achievements and future directions. IET Comput Vis 9(4):614–626

[4] Déniz O, Bueno G, Salido J, De la Torre F (2011) Face recognition using histograms of oriented gradients. Pattern Recognit. Lett. 32(12):1598–1603

[5] Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: IEEE Computer society conference on computer vision and pattern recognition, Kauai, HI, USA, December, pp 511–518

[6] Zhang K, Zhang Z, Li Z, Qiao Y (2016) Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process Lett 23(10):1499–1503

[7] Ahonen T, Hadid A, Pietikäinen M (2004) Face recognition with local binary patterns. In: Pajdla T, Matas J (eds) 8th European conference on computer vision, Prague, Czech Republic, May, pp 469–481

[8] He X, Yan S, Hu Y, Niyogi P, Zhang H-J (2005) Face recognition using Laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340

[9] Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227

[10] Liu Z, Luo P, Wang X, Tang X (2015) Deep learning face attributes in the wild. In: IEEE International conference on computer vision, Santiago, Chile, December, pp 3730–3738

[11] Taigman Y, Yang M, Ranzato M, Wolf L (2014) DeepFace: closing the gap to human-level performance in face verification. In: IEEE Conference on computer vision and pattern recognition (CVPR), Columbus, OH, USA, June, pp 1701–1708

[12] He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: IEEE Conference on computer vision and pattern recognition, Las Vegas, Nevada, USA, July, pp 770–778

[13] Schroff F, Kalenichenko D, Philbin J (2015) FaceNet: a unified embedding for face recognition and clustering. In: IEEE Conference on computer vision and pattern recognition (CVPR), Boston, MA, USA, June 2015, pp 815–823

[14] Amos B, Ludwiczuk B, Satyanarayanan M (2016) OpenFace: a general-purpose face recognition library with mobile applications. CMU Sch Comput Sci 6(2):1–20

[15] Deng J, Guo J, Xue N, Zafeiriou S (2019) ArcFace: additive angular margin loss for deep face recognition. In: Presented at the IEEE/CVF conference on computer vision and pattern recognition, Long Beach, CA, USA, June 2019, pp 4690–4699

[16] Tan M, Le QV (2019) EfficientNet: rethinking model scaling for convolutional neural networks. In: 36th international conference on machine learning, Long Beach, CA, USA, June, pp 6105–6114