A Proposed Framework: Face Recognition With Deep Learning

Face recognition is the capability to ascertain the identification of a person solitary or amidst multitudes of individuals. In lieu to this, deep learning has dominated and it has been used in recent years due to its momentous performance to solve the face recognition challenges using convolutional neural networks (CNN). It is a technology with enormous capabilities and diversities used in computer vison problems such as modelling and saliency detection, semantic segmentation, handwriting digital recognition, emotion recognition and many more. CNN architectures such has Alex Net, VGG are the practically known architectures that have immensely prompt new dataset for CNN model designs. This paper contributes to actualization of a propose CNN based on a pre-trained VGG Face for face recognition from set of faces tracked in video or image capture achieving a 97% accuracy. Also, implementing the use of metric learning to actualized a discriminative feature from our instances.

[1]  Yanning Zhang,et al.  Face Recognition via Deep Learning Using Data Augmentation Based on Orthogonal Experiments , 2019, Electronics.

[2]  Stefanos Zafeiriou,et al.  ArcFace: Additive Angular Margin Loss for Deep Face Recognition , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Sudhir Bussa,et al.  Smart Attendance System using OPENCV based on Facial Recognition , 2020 .

[4]  Stephen Balaban,et al.  Deep learning and face recognition: the state of the art , 2015, Defense + Security Symposium.

[5]  Tieniu Tan,et al.  A Light CNN for Deep Face Representation With Noisy Labels , 2015, IEEE Transactions on Information Forensics and Security.

[6]  Sanjay Singh,et al.  Face Detection and Tagging Using Deep Learning , 2018, 2018 International Conference on Computer, Communication, and Signal Processing (ICCCSP).

[7]  Feiyue Huang,et al.  CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Richa Singh,et al.  Unravelling Robustness of Deep Learning based Face Recognition Against Adversarial Attacks , 2018, AAAI.

[9]  Vinit Kumar Gunjan,et al.  Deep Learning Based Representation for Face Recognition , 2019, Lecture Notes in Electrical Engineering.

[10]  Darko Stefanovic,et al.  FaceTime — Deep learning based face recognition attendance system , 2017, 2017 IEEE 15th International Symposium on Intelligent Systems and Informatics (SISY).

[11]  Afef Abdelkrim,et al.  Machine learning framework for image classification , 2016, 2017 International Conference on Information and Digital Technologies (IDT).

[12]  Dacheng Tao,et al.  Robust Face Recognition via Multimodal Deep Face Representation , 2015, IEEE Transactions on Multimedia.

[13]  Nurul Hazirah Bt Indra,et al.  A study on Image Classification based on Deep Learning and Tensorflow , 2019 .

[14]  Payal Pahwa,et al.  Analysis of Nonlinear Activation Functions for Classification Tasks Using Convolutional Neural Networks , 2019 .

[15]  Xiaogang Wang,et al.  Hybrid Deep Learning for Face Verification , 2013, 2013 IEEE International Conference on Computer Vision.

[16]  Namita Mittal,et al.  Using CNN for facial expression recognition: a study of the effects of kernel size and number of filters on accuracy , 2019, The Visual Computer.

[17]  Mahdi Hashemi,et al.  RETRACTED ARTICLE: Criminal tendency detection from facial images and the gender bias effect , 2020, Journal of Big Data.

[18]  Nikolaos Doulamis,et al.  Deep Learning for Computer Vision: A Brief Review , 2018, Comput. Intell. Neurosci..

[19]  William J. Christmas,et al.  When Face Recognition Meets with Deep Learning: An Evaluation of Convolutional Neural Networks for Face Recognition , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).

[20]  Vinayak Ashok Bharadi,et al.  Image Classification Using Deep Learning , 2017 .

[21]  Mei Wang,et al.  Deep Face Recognition: A Survey , 2018, Neurocomputing.

[22]  Chang Huang,et al.  Targeting Ultimate Accuracy: Face Recognition via Deep Embedding , 2015, ArXiv.

[23]  Ouarda Assas,et al.  Multimodal Face and Iris Recognition with Adaptive Score Normalization Using Several Comparative Methods , 2019, Indian Journal of Science and Technology.

[24]  Klemen Grm,et al.  Strengths and weaknesses of deep learning models for face recognition against image degradations , 2017, IET Biom..

[25]  Hassan Ugail,et al.  Deep face recognition using imperfect facial data , 2019, Future Gener. Comput. Syst..

[26]  Rama Chellappa,et al.  HyperFace: A Deep Multi-Task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.