Face Recognition using Machine Learning Algorithms


Amirhosein Dastgiri, Pouria Jafarinamin,Sami Kamarbaste,Mahdi Gholizade,




face recognition, machine learning al gorithms,image process,


Face recognition is one of the most challenging issues in analyzing images. Face recognition technology is one of the fastest technologies that do the identification process without having the slightest disturbance to the person. Face recognition today has found many applications that can be used for faces recognition, military issues, legal issues, image retrieval, identification of protagonists, video images, and so on. Face recognition is considered as one of the smart computer analysis scenarios. There are always improvements in this area that make these improvements accurate in identifying facial expressions. Accordingly, the present paper seeks to study facial recognition using machine learning algorithms. Time information has useful features for recognizing facial expressions. However, a lot of effort is needed to manually design features. In this paper, to reduce these factors, a machine learning technique is selected, which is an automated tool that extracts useful features from raw data. Using machine learning methods can be considered as a more effective way. In this paper, a method based on machine learning algorithms for face recognition is presented. The proposed algorithms perform the unknown image by comparing it with known and stored images in databases and also obtaining information from a person familiar with the process of face recognition. The results show that the proposed method has high accuracy compared to other previous methods.


I.E. García Amaro, M. A. Nuño-Maganda and M. Morales-Sandoval, “Evaluationof machine learning techniques for face detection and recognition,”CONIELECOMP 2012, 22nd International Conference on Electrical
Communications and Computers, Cholula, Puebla, pp. 213-218, 2012.
II.F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815
-823, 2015.
III.G. Zeng, J. Zhou, X. Jia, W. Xie and L. Shen, “Hand-Crafted Feature Guided Deep Learning for Facial Expression Recognition,”2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018),Xi’an, pp. 423-430, 2018.
IV.Kong, X., Gong, S., Su, L., Howard, N., & Kong, Y. Automatic Detection of Acromegaly From Facial Photographs Using Machine Learning Methods. EBioMedicine,27, 94-102, 2017.
V.Kortylewski, B. Egger and A. Schneider. Empirically Analyzing the Effect of Dataset Biases on Deep Face Recognition Systems.Computer Vision and Pattern Recognition (CVPR), 2018.
VI.M.Saraswathi and Dr. S. Sivakumari, “Evaluation of PCA and LDA techniques for Face recognition using ORL face database”, (IJCSIT) International Journal of Computer Science and Information Technologies,
Volume 6 (1), pp. 810-813,2015.
VII.P. Jonathon Phillips, Amy N. Yates, Ying Hu, Carina A. Hahn, Eilidh Noyes,Kelsey Jackson, Jacqueline G. Cavazos, Géraldine Jeckeln, Rajeev Ranjan,Swami Sankaranarayanan, Jun-Cheng Chen, Carlos D. Castillo, Rama Chellappa, David White, and Alice J. O’Toole. Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms. PNAS June 12, 115 (24) 6171-6176, 2018.
VIII.Praahas Amin, Prithvi, Roshni Fernandes, Shivaraj S B,Sneha P. Machine Learning based Face Recognition System for Virtual Assistant. International Research Journal of Engineering and Technology(IRJET). Volume: 05 Issue: 05,pp. 33-55, 2015.
IX.Q. Cao, L. Shen, W. Xie, O. M. Parkhi, and A. Zisserman, “VGGFace2: Adataset for recognisin
g faces across pose and age,” arXiv preprintarXiv:1710.08092, 2017.
X.Sanjeev Kumar and Harpreet Kaur, “Face Recognition Techniques:Classification and Comparisons”, International Journal of Information Technology and Knowledge Management July-December, Vol
ume 5, No. 2, pp.361-363, 2012.
XI.V. P. Vishwakarma, “Deterministic learning machine for face recognition with multi-model feature extraction,” Ninth International Conference on Contemporary Computing (IC3), Noida, pp. 1-6, 2016.
XII.X. Han and Q. Du (2018). Research on face recognition based on deep learning.2018 Sixth International Conference on Digital Information, Networking, and Wireless Communications (DINWC), Beirut, pp. 53
-58, 2018.
XIII.Y. Guo, L. Zhang, Y. Hu, X. He, and J. Gao, “Ms-celeb-1m: Adataset and benchmark for large-
scale face recognition,” inEuropeanConference on Computer Vision. Springer, pp. 87–102, 2016.
XIV.Y. Li, B. Sun, T. Wu, and Y. Wang, “Face detection with end-to-endintegration of a convnet and a 3d model,”European ConferenceonComputer Vision (ECCV), 2016.
XV.Y. Li, W. Shen, X. Shi, and Z. Zhang.Ensemble of randomized linear discriminant analysis for face recognition with single sample per person,” in Proceedings of IEEE International Conference and Work
shops on Automatic Face and Gesture Recognition, pp. 1–8, Shanghai, 2013.
Amirhosein Dastgiri, Pouria Jafarinamin, Sami Kamarbaste, Mahdi Gholizade View Download