[1] S.H. Abdurrahim, S.A. Samad and A.B. Huddin, Review on the effects of age, gender, and race demographics on automatic face recognition, Vis. Comput. 34(11) (2018) 1617-–1630.
[2] O.N.A. AL-Allaf, Review of face detection systems based artificial neural networks algorithms, Int. J. Multimed. Appl. 6(1) (2014) 1—16.
[3] V. Albiero, K.S. Krishnapriya, K. Vangara, K. Zhang, M.C. King and K.W. Bowyer, Analysis of gender inequality in face recognition accuracy, Proc. - 2020 IEEE Winter Conf. Appl. Comput. Vis. Workshops, 2020, pp. 81—89.
[4] J. Alghamdi, R. Alharthi, R. Alghamdi, W. Alsubaie, R.Alsubaie, D. Alqahtani and R. Alshammari, A survey on face recognition algorithms, In 2020 3rd Int. Conf. Comput. Appl. Info. Secur. (ICCAIS), IEEE, 2020, pp. 1–5.
[5] O.Y. Al-Jarrah, P.D. Yoo, S. Muhaidat, G.K. Karagiannidis and K. Taha, Efficient machine learning for big data: A review, Big Data Res. 2(3) (2015) 87—93.
[6] F. Alonso-Fernandez, K. Hernandez-Diaz, S. Ramis, F.J. Perales and J. Bigun, Facial masks and soft-biometrics: Leveraging face recognition CNNs for age and gender prediction on mobile ocular images, IET Biometrics 10(5) (2021) 562-–580.
[7] R.R. Atallah, A. Kamsin, M.A. Ismail, S.A. Abdelrahman and S. Zerdoumi, Face recognition and age estimation implications of changes in facial features: A critical review study, IEEE Access, 6 (2018) 28290—28304.
[8] S. Balaban, Deep learning and face recognition: the state of the art, Biometric Surveill. Technol. Hum. Act. Identif. XII 9457 (2015) 94570B.
[9] P. Belhumeur, J. Hespanha and D. Kriegman, OEigenfaces vs. Fisherfaces: Recognition using class specific linear projection, IEEE Trans. Pattern Anal. Machine Intell. 1997, pp. 711–720.
[10] M.K. Benkaddour, S. Lahlali and M. Trabelsi, Human age and gender classification using convolutional neural network, 2020 2nd Int. Work. Human-Centric Smart Environ. Heal. Well-Being, IHSH 2020, (2021) 215-–220.
[11] L. Best-Rowden, H. Han, C. Otto, B.F. Klare and A.K. Jain, Unconstrained face recognition: Identifying a person of interest from a media collection, IEEE Trans. Inf. Forensics Secur. 9(12) (2014) 2144—2157.
[12] D. Bhati and V. Gupta, Survey –a comparative analysis of face recognition technique, Int. J. Eng. Res. Gen. Sci. 3(2) (2015) 597—609.
[13] A. Clap´es, O. Bilici, D. Temirova, E. Avots, G. Anbarjafari and S. Escalera, From apparent to real age: gender, age, ethnic, makeup, and expression bias analysis in real age estimation, Proc. IEEE Conf. Comput. Vision Pattern Recogn. Workshops, 2018, pp. 2373–2382.
[14] A. Dehghan, E.G. Ortiz, G. Shu and S.Z. Masood, DAGER: Deep age, gender and emotion recognition using convolutional neural network, arXiv preprint arXiv:1702.04280, (2017).
[15] H. Deng, Z. Feng, G. Qian, X. Lv, H. Li and G. Li, MFCosface: a masked-face recognition algorithm based on large margin cosine loss, Appl. Sci. 11(16) (2021).
[16] A. Dhomne, R. Kumar and V. Bhan, Gender recognition through face using deep learning, Procedia Comput. Sci. 132 (2018) 2-–10.
[17] J. Galbally, S. Marcel and J. Fierrez, Biometric antispoofing methods: A survey in face recognition, IEEE Access, 2 (2014) 1530-–1552.
[18] G. Guo and N. Zhang, A survey on deep learning based face recognition, Comput. Vis. Image Underst.189 (2019) 102805.
[19] W. Hariri, Efficient masked face recognition method during the COVID-19 pandemic, Signal, Image Video Process, (2021) 1–8.
[20] M. Hassaballah and S. Aly, Face recognition: Challenges, achievements and future directions, IET Comput. Vis. 9(4) (2015) 614–626.
[21] C.Y. Hsu, L.E. Lin and C.H. Lin, Age and gender recognition with random occluded data augmentation on facial images, Multimed. Tools Appl. 80(8) (2021) 11631-–11653.
[22] G.B. Huang, M. Ramesh, T. Berg and E. Learned-Miller, Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments, University of Massachusetts, Amherst, Technical Report 07-49, 2007.
[23] M. Jacquet and C. Champod, Automated face recognition in forensic science: Review and perspectives, Forensic Sci. Int. 307 (2020).
[24] B. Kamgar-Parsi, W. Lawson and B. Kamgar-Parsi, Toward development of a face recognition system for watchlist surveillance, IEEE Trans. Pattern Anal. Mach. Intell. 33(10) (2011) 1925-–1937.
[25] M.M. Kasar, D. Bhattacharyya and T.H. Kim, Face recognition using neural network: A review, Int. J. Secur. Appl. 10(3) (2016) 81—100.
[26] A.M.U.D. Khanday, A. Amin, I. Manzoor and R. Bashir, Face recognition techniques: A critical review, STM Journals, 5(2) (2018) 24-–30.
[27] K.G. Kim, Book review: Deep learning, Healthcare Inf. Res. 22(4) (2016) 351–354.
[28] M. Lal, K. Kumar, R.H. Arain, A. Maitlo, S.A. Ruk and H. Shaikh, Study of face recognition techniques: A survey, Int. J. Adv. Comput. Sci. Appl. 9(6) (2018) 42–49.
[29] S.Z. Li and A.K. Jain, Handbook of Face Recognition, Springer Science & Business Media, 2014.
[30] P. Li, L. Prieto, D. Mery and P. Flynn, Face recognition in low-quality images: A survey, arXiv preprint arXiv:1805.11519, 1(1) (2018).
[31] K.G. Liakos, P. Busato, D. Moshou, S. Pearson and D. Bochtis, Machine learning in agriculture: A review, Sensors 18(8) (2018) 1-–29.
[32] S. Liu and S.S. Agaian, COVID-19 face mask detection in a crowd using multi-model based on YOLOv3 and hand-crafted features, Multimodal Image Exploitation and Learning 2021, International Society for Optics and Photonics, 11734 (2021).
[33] Z. Liu, P. Luo, X. Wang and X. Tang, Deep learning face attributes in the wild, Proc. Int. Conf. Comput. Vision . 2015.
[34] J.J. Lv, C. Cheng, G.D. Tian, X.D. Zhou and X. Zhou, Landmark perturbation-based data augmentation for unconstrained face recognition, Signal Process. Image Commun. 47 (2016) 465–475.
[35] A. Mallikarjuna Reddy, V. Venkata Krishna and L. Sumalatha, Face recognition approaches: A survey, Int. J. Eng. Technol. 7(4-6) (2018) 117-–121.
[36] M. Mann and M. Smith, Automated facial recognition technology: Recent developments and regulatory options, Univ. N.S.W. Law J. 40(1) (2017).
[37] I. Masi, Y. Wu, T. Hassner and P. Natarajan, Deep face recognition: A survey, Proc. 31st Conf. Graph. Patterns Images, SIBGRAPI 2018, (2019) 471-–478.
[38] S.E. Mason, Age and gender as factors in facial recognition and identification, Exp. Ageing Res. 12(3) (1986) 151—154
[39] A.H. Miry, Face detection based on multi facial feature using fuzzy logic, Al-Mansour J. 21 (2014) 15–30.
[40] R. Mu and X. Zeng, A review of deep learning research, KSII Trans. Internet Inf. Syst. 13(4) (2019) 1738—1764.
[41] J.S. Nayak and M. Indiramma, An approach to enhance age invariant face recognition performance based on gender classification, J. King Saud. Univ. Comput. Inf. Sci. (2021).
[42] J.I. Olszewska, Automated Face Recognition: Challenges and Solutions, IntechOpen 2016.
[43] K. Panetta, Q. Wan, S. Agaian, S. Rajeev, S. Kamath, R. Rajendran, S.P. Rao, A. Kaszowska, H.A. Taylor, A. Samani and X. Yuan, A comprehensive database for benchmarking imaging systems, IEEE Trans. Pattern Anal. Machine Intell. 42(3) (2018) 509–520.
[44] N. Pinto, Z. Stone, T. Zickler and D. Cox, Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on Facebook, IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work (2011) 35–42.
[45] R. Ramachandra and C. Busch, Presentation attack detection methods for face recognition systems: A comprehensive survey, ACM Comput. Surv. 50(1) (2017).
[46] R. Ranjan, A. Bansal, J. Zheng, H. Xu, J. Gleason, B. Lu, A. Nanduri, J.C. Chen, C.D. Castillo and R. Chellappa, A fast and accurate system for face detection, identification, and verification, IEEE Transactions on Biometrics, Behav. Ident. Sci. 1(2) (2019) 82–96.
[47] S.K. Rath and S.S. Rautaray, A survey on face detection and recognition techniques in different application domain, Int. J. Mod. Educ. Comput. Sci. 6(8) (2014) 34–44.
[48] P. Rodr´ıguez, G. Cucurull, J.M. Gonfaus, F.X. Roca and J. Gonzalez, Age and gender recognition in the wild with deep attention, Pattern Recognit. 72 (2017) 563-–571.
[49] R. Rothe, R. Timofte and L. Van Gool, Deep expectation of real and apparent age from a single image without facial landmarks, Int. J. Comput. Vision 126(2–4) (2018) 144–157.
[50] I. Sajid, M.M. Ahmed, I. Taj, M. Humayun and F. Hameed, Design of high performance FPGA based face recognition system, Prog. Electromag. Res. Symp. Proc. 2008, pp. 504–510.
[51] M. Sharif, F. Naz, M. Yasmin, M.A. Shahid and A. Rehman, Face recognition: A survey, J. Eng. Sci. Tech. Rev. 10(2) (2017) 166—177.
[52] P.P. Shinde and S. Shah, A review of machine learning and deep learning applications, Proc. 2018 4th Int. Conf. Comput. Commun. Control Autom. ICCUBEA 2018, pp. 1-–6.
[53] S. Singh and S.V.A.V. Prasad, Techniques and challenges of face recognition: A critical review, Procedia Comput. Sci. 143 (2018) 536—543.
[54] R. Sirkeviciute, Recognition of emotions from facial expressions: The role of gender, age, personality, and empathy, PhD diss., National College of Ireland, 2016.
[55] D.V. Solanki and A.M. Kothari, Comparative survey of face recognition techniques, Int. J. Adv. Eng. Res. Dev. 3(02) (2015).
[56] K. Solanki and P. Pittalia, Review of face recognition techniques, Int. J. Comput. Appl. 133(12) (2016) 20—24.
[57] M. Taskiran, N. Kahraman and C.E. Erdem, Face recognition: Past, present and future (a review), Digit. Signal Process. A Rev. J. 106 (2020) 102809.
[58] D.S. Trigueros, L. Meng and M. Hartnett, Face recognition: From traditional to deep learning methods, arXiv preprint arXiv:1811.00116, (2018).
[59] R. Vargas, A. Mosavi and R. Ruiz, Deep learning: a review, Adv. Intell. Syst. Comput. 2017 (2017).
[60] Z. Wang, G. Wang, B. Huang, Z. Xiong, Q. Hong, H. Wu, P. Yi, K. Jiang, N. Wang, Y. Pei, H. Chen, Y. Miao, Z. Huang and J. Liang, Masked face recognition dataset and application, RMFD, 2020.
[61] W. W´ojcik, K. Gromaszek and M. Junisbekov, Face recognition: Issues, methods and alternative applications, Face Recognit. - Semisupervised Classif. Subsp. Proj. Eval. Methods, (2016) 7–28.
[62] S. Wu and D. Wang, Effect of subject’s age and gender on face recognition results, J. Vis. Commun. Image Represent 60 (2019) 116—122.
[63] Y. Xu, Z. Li, J. Yang and D. Zhang, A survey of dictionary learning algorithms for face recognition, IEEE Access 5 (2017) 8502—8514.
[64] CBSR, CASIA Gait Database, Center for Biometrics and Security Research, 2005.