scholarly journals A Study on various state of the art of the Art Face Recognition System using Deep Learning Techniques

2019 ◽  
pp. 1590-1600 ◽  
Author(s):  
Sukhada Chokkadi ◽  
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
State Of The Art ◽  
Recognition System ◽  
Learning Techniques ◽  
Face Recognition System

scholarly journals The Elements of End-to-end Deep Face Recognition: A Survey of Recent Advances

2022 ◽  
Author(s):  
Hang Du ◽  
Hailin Shi ◽  
Dan Zeng ◽  
Xiao-Ping Zhang ◽  
Tao Mei
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Face Detection ◽  
Recognition System ◽  
Face Alignment ◽  
Face Representation ◽  
Learning Techniques ◽  
Face Recognition System ◽  
The Face ◽  
End To End

Face recognition is one of the most popular and long-standing topics in computer vision. With the recent development of deep learning techniques and large-scale datasets, deep face recognition has made remarkable progress and been widely used in many real-world applications. Given a natural image or video frame as input, an end-to-end deep face recognition system outputs the face feature for recognition. To achieve this, a typical end-to-end system is built with three key elements: face detection, face alignment, and face representation. The face detection locates faces in the image or frame. Then, the face alignment is proceeded to calibrate the faces to the canonical view and crop them with a normalized pixel size. Finally, in the stage of face representation, the discriminative features are extracted from the aligned face for recognition. Nowadays, all of the three elements are fulfilled by the technique of deep convolutional neural network. In this survey article, we present a comprehensive review about the recent advance of each element of the end-to-end deep face recognition, since the thriving deep learning techniques have greatly improved the capability of them. To start with, we present an overview of the end-to-end deep face recognition. Then, we review the advance of each element, respectively, covering many aspects such as the to-date algorithm designs, evaluation metrics, datasets, performance comparison, existing challenges, and promising directions for future research. Also, we provide a detailed discussion about the effect of each element on its subsequent elements and the holistic system. Through this survey, we wish to bring contributions in two aspects: first, readers can conveniently identify the methods which are quite strong-baseline style in the subcategory for further exploration; second, one can also employ suitable methods for establishing a state-of-the-art end-to-end face recognition system from scratch.

scholarly journals Design of a Face Recognition System based on Convolutional Neural Network (CNN)

2020 ◽  
Vol 10 (3) ◽  
pp. 5608-5612 ◽  
Author(s):  
Y. Said ◽  
M. Barr ◽  
H. E. Ahmed
Keyword(s):  
Face Recognition ◽  
State Of The Art ◽  
Recognition System ◽  
Great Variability ◽  
Artificial Vision ◽  
Surveillance Systems ◽  
Distributed Network ◽  
Biometric System ◽  
Face Recognition System ◽  
Deep Learning Model

Face recognition is an important function of video surveillance systems, enabling verification and identification of people who appear in a scene often captured by a distributed network of cameras. The recognition of people from the faces in images arouses great interest in the scientific community, partly because of the application interests but also because of the challenge that this represents for artificial vision algorithms. They must be able to cope with the great variability of the aspects of the faces themselves as well as the variations of the shooting parameters (pose, lighting, haircut, expression, background, etc.). This paper aims to develop a face recognition application for a biometric system based on Convolutional Neural Networks. It proposes a structure of a Deep Learning model which allows improving the existing state-of-the-art precision and processing time.

scholarly journals A face recognition system using convolutional feature extraction with linear collaborative discriminant regression classification

2022 ◽  
Vol 12 (2) ◽  
pp. 1468
Author(s):  
Sangamesh Hosgurmath ◽  
Viswanatha Vanjre Mallappa ◽  
Nagaraj B. Patil ◽  
Vishwanath Petli
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Distance Ratio ◽  
Research Areas ◽  
Lighting Conditions ◽  
Learning Techniques ◽  
Face Recognition System ◽  
The Face

Face recognition is one of the important biometric authentication research areas for security purposes in many fields such as pattern recognition and image processing. However, the human face recognitions have the major problem in machine learning and deep learning techniques, since input images vary with poses of people, different lighting conditions, various expressions, ages as well as illumination conditions and it makes the face recognition process poor in accuracy. In the present research, the resolution of the image patches is reduced by the max pooling layer in convolutional neural network (CNN) and also used to make the model robust than other traditional feature extraction technique called local multiple pattern (LMP). The extracted features are fed into the linear collaborative discriminant regression classification (LCDRC) for final face recognition. Due to optimization using CNN in LCDRC, the distance ratio between the classes has maximized and the distance of the features inside the class reduces. The results stated that the CNN-LCDRC achieved 93.10% and 87.60% of mean recognition accuracy, where traditional LCDRC achieved 83.35% and 77.70% of mean recognition accuracy on ORL and YALE databases respectively for the training number 8 (i.e. 80% of training and 20% of testing data).

An effective face recognition system based on Cloud based IoT with a deep learning model

2021 ◽  
Vol 81 ◽  
pp. 103726
Author(s):  
Deepika Chauhan ◽  
Ashok Kumar ◽  
Pradeep Bedi ◽  
Vijay Anant Athavale ◽  
D. Veeraiah ◽  
...  
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Recognition System ◽  
Learning Model ◽  
Face Recognition System ◽  
Deep Learning Model

A Deep Learning based Approach for Real Time Face Recognition System

2021 ◽  
Author(s):  
Tanusree Das Tithy ◽  
Soarov Chakraborty ◽  
Rabaya Islam ◽  
Abdul Aziz
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Real Time ◽  
Recognition System ◽  
Face Recognition System

scholarly journals Transition from Holistic to Deep learning Face Recognition Methods

2019 ◽  
Vol 8 (4) ◽  
pp. 3111-3116
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Healthcare Access ◽  
Hybrid Approach ◽  
Recognition System ◽  
Learning Approach ◽  
Holistic Learning ◽  
Face Representation ◽  
Face Recognition System ◽  
The Face

Face recognition, the fastest growing biometric technology of computer vision, made a breakthrough in the field of security, healthcare, access control and marketing etc. This technology helps in automatically discern and identify the faces for authentication by comparing available digital image of faces. Various algorithms have been developed for enhancing the performance of face recognition system. The face authentication system entails three major steps, face detection, feature extraction and face recognition. This paper provides some of the major milestones of face representation for recognition like holistic learning approach, feature based approach, hybrid approach and deep learning approach. The various techniques under these categories are reviewed. Finally, implemented face recognition using convolution neural network (CNN). In this method, the image is captured through webcam for the dataset preparation. The detection is carried out by CNN cascade, followed by face landmark and face embedding by FaceNet CNN. Recognition of face is performed after training the network. Implemented faces recognition successfully and accurately for smaller dataset.

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