References
[1]. Abuzneid, M. A., & Mahmood, A. (2018). Enhanced
human face recognition using LBPH descriptor, multi-KNN,
and back-propagation neural network. IEEE Access, 6,
20641-20651. https://doi.org/10.1109/ACCESS.2018.28
25310
[2]. Ali, T., Veldhuis, R. N. J., & Spreeuwers, L. J. (2010).
Forensic Face Recognition: A Survey (CTIT Technical Report
Series; No. TR-CTIT-10-40). Centre for Telematics and
Information Technology (CTIT).
[3]. Ayyavoo, T., & Jayasudha, J. S. (2013, December).
Face recognition using enhanced energy of Discrete
Wavelet Transform. In 2013 International Conference on
Control Communication and Computing (ICCC) (pp. 415-
419). IEEE. https://doi.org/10.1109/ICCC.2013.6731690
[4]. Bakshi, N., & Prabhu, V. (2017, December). Face
recognition system for access control using principal
component analysis. In 2017 International Conference on Intelligent Communication and Computational
Techniques(ICCT) (pp.145-150). IEEE.
https://doi.org/10.1109/INTEL CCT.2017.8324035
[5]. Bhat, F. A., & Wani, M. A. (2014, December).
Performance comparison of major classical face
recognition techniques. In 2014 13th International
Conference on Machine Learning and Applications (pp.
521-528). IEEE. https://doi.org/10.1109/ICMLA.2014.91
[6]. Hassaballah, M., & Aly, S. (2015). Face recognition:
challenges, achievements and future directions. IET
Computer Vision, 9(4), 614-626. https://doi.org/10.1049/
iet-cvi.2014.0084
[7] Fan, C. L., & Chen, X. T. (2012, May). Research of face
recognition based on wavelet transform and principal component analysis. In 2012 8th International Conference
on Natural Computation (pp. 575-578) IEEE. https://doi.o
rg/10.1109/ICNC.2012.6234703
[8]. Deng, W., Hu, J., & Guo, J. (2017). Face recognition via
collaborative representation: Its discriminant nature and
superposed representation. IEEE Transactions on Pattern
Analysis and Machine Intelligence, 40(10), 2513-2521.
https://doi.org/10.1109/TPAMI.2017.2757923
[9]. Hickman, D., Goode, A., & Gandolfi, P. (2005, June).
Forensic image comparison techniques. In the IEE
International Symposium on Imaging for Crime Detection
and Prevention ICDP 2005 (pp. 99-103). https://doi.org/ 10.
1049/ic:20050078
[10]. Jain, A. K., Klare, B., & Park, U. (2011, March). Face
recognition: Some challenges in forensics. In Face and
Gesture 2011 (pp. 726-733). IEEE. https://doi.org/10.1109/F
G.2011.5771338
[11]. Kumar, M.P., Sravan, R.K., & Aishwarya, K. (2015).
Artificial neural networks for face recognition using PCA and
BPNN. In TENCON 2015-2015 IEEE Region 10 Conference
(pp. 1-6). IEEE. https://doi.org/10.1109/TENCON.2015.737
3165
[12]. Lohiya, R., & Shah, P. (2015). Face recognition
techniques: A survey for forensic applications. International
Journal of Advanced Research in Computer Engineering
& Technology (IJARCET), 4(4).
[13]. Phillips, P. J., Yates, A. N., Hu, Y., Hahn, C. A., Noyes, E., Jackson, K., ... & Chen, J. C. (2018). Face recognition
accuracy of forensic examiners, superrecognizers, and
face recognition algorithms. In Proceedings of the
National Academy of Sciences, 115(24), 6171-6176. https
://doi.org/ 10.1073/pnas.1721355115
[14]. Salici, A., & Ciampini, C. (2017, September).
Automatic face recognition and identification tools in the
forensic science domain. In International Tyrrhenian
Workshop on Digital Communication (pp. 8-17). Cham:
Springer. https://doi.org/10.1007/978-3-319-67639-5_2
[15]. Serajeh, R., Mohammadzadeh, Z., and
Ghavitandarjazi, H. (2017). Face recognition in
uncontrolled conditions. In IEEE 4th International
Conference on Knowledge-Based Engineering and
Innovation (KBEI) (pp. 0902-0906). https://doi.org/10.1109/K
BEI.2017.83249 26
[16]. Shnain, N. A., Lu, S. F., & Hussain, Z. M. (2017,
December). HOS image similarity measure for human face
recognition. In 2017 3rd IEEE International Conference on
Computer and Communications (ICCC) (pp. 1621-1625).
IEEE. https://doi.org/10.1109/CompComm.2017.8322814
[17]. Spaun, N. A. (2011). Face recognition in forensic
science. In Handbook of Face Recognition (pp. 655-670).
London: Springer. https://doi.org/10.1007/978-0-85729-93
2-1_26
[18]. Turan, C. (2017, November). Robust face recognition
via sparse reconstruction vector. In 2017 13th International
Conference on Electronics, Computer and Computation
(ICECCO) (pp. 1-4). IEEE. https://doi.org/10.1109/ICECC
O.2017.8333342
[19]. Turk, M. A., & Pentland, A. P. (1991, January). Face
recognition using eigenfaces. In Proceedings of1991 IEEE
computer society conference on computer vision and
pattern recognition (pp. 586-587). IEEE Computer Society.
[20]. Tvakolian, N., Nazemi, A., & Azimifar, Z. (2017,
October). Authentication based on face recognition under
uncontrolled conditions. In 2017 Artificial Intelligence and
Signal Processing Conference (AISP) (pp. 113-117). IEEE.
https://doi.org/10.1109/AISP.2017.8324120
[21]. Wright, J., Yang, A. Y., Ganesh, A., Sastry, S. S., & Ma, Y.
(2008). Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine
Intelligence, 31(2), 210-227. https://doi.org/10.1109/TPA
MI.2008.79
[22]. Wu, M., Li, S., & Hu, J. (2017, December). Extended
class-wise sparse representation for face recognition. In
2017 3rd IEEE International Conference on Computer and
Communications (ICCC) (pp. 1611-1615). IEEE. https://doi.org/10.1109/CompComm.2017.8322812
[23]. Xiao-Qiang, Y., & Xiao-Bing, Z. (2017, December).
Face recognition research based on the fusion of layered
LBP feature. In 2017 International Conference on Industrial
Informatics-Computing Technology, Intelligent Technology,
Industrial Information Integration (ICIICII) (pp. 75-78). IEEE.
https://doi.org/10.1109/ICIICII.2017.13
