scholarly journals Low Resolution Face Recognition Using Generative Adversarial Network (Gan)

2020 ◽  
Author(s):  
Howard Martin ◽  
Suharjito
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Fine Tuning ◽  
Low Resolution ◽  
Generative Adversarial Network ◽  
Resolution Image ◽  
The Past ◽  
Adversarial Network ◽  
Face Recognition System ◽  
Sr Method

Abstract Although face recognition system has achieved a very good performance in the past years, but Low Resolution Face Recognition (LRFR) is still challenging because low resolution image would decrease the accuracy. This research aimed to solved and get the best SR method to solved LRFR problem. YTF dataset used for fine tuning SR methods. While LFW dataset used for fine tuning and evaluating FaceNet model. The images would be increased using Res-Net GAN and RRDB GAN. Then the images would be recognized using FaceNet. The images that had been increased by RRDB GAN reached the highest accuracy 98.96 %.

scholarly journals Low Resolution Face Recognition Using Generative Adversarial Network (GAN)

2020 ◽  
Author(s):  
Howard Martin ◽  
Suharjito
Keyword(s):  
Face Recognition ◽  
Super Resolution ◽  
Image Data ◽  
Face Image ◽  
Image Resolution ◽  
Low Resolution ◽  
Generative Adversarial Network ◽  
Resolution Image ◽  
Surveillance Camera ◽  
Adversarial Network

Abstract Face recognition has a lot of use on smartphone authentication, finding people, etc. Nowadays, face recognition with a constrained environment has achieved very good performance on accuracy. However, the accuracy of existing face recognition methods will gradually decrease when using a dataset with an unconstrained environment. Face image with an unconstrained environment is usually taken from a surveillance camera. In general, surveillance cameras will be placed on the corner of a room or even on the street. So, the image resolution will be low. Low-resolution image will cause the face very hard to be recognized and the accuracy will eventually decrease. That is the main reason why increasing the accuracy of the Low-Resolution Face Recognition (LRFR) problem is still challenging. This research aimed to solve the Low-Resolution Face Recognition (LRFR) problem. The datasets are YouTube Faces Database (YTF) and Labelled Faces in The Wild (LFW). In this research, face image resolution would be decreased using bicubic linear and became the low-resolution image data. Then super resolution methods as the preprocessing step would increase the image resolution. Super resolution methods used in this research are Super resolution GAN (SRGAN) [1] and Enhanced Super resolution GAN (ESRGAN) [2]. These methods would be compared to reach a better accuracy on solving LRFR problem. After increased the image resolution, the image would be recognized using FaceNet. This research concluded that using super resolution as the preprocessing step for LRFR problem has achieved a higher accuracy compared to [3]. The highest accuracy achieved by using ESRGAN as the preprocessing and FaceNet for face recognition with accuracy of 98.96 % and Validation rate 96.757 %.

scholarly journals Transfer learning using AlexNet Convolutional Neural Network for Face Recognition

2020 ◽  
Vol 9 (11) ◽  
pp. 285-294
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Recognition System ◽  
Training Data ◽  
Fine Tuning ◽  
Data Sets ◽  
Learning Method ◽  
Face Recognition System

This research is aimed to achieve high-precision accuracy and for face recognition system. Convolution Neural Network is one of the Deep Learning approaches and has demonstrated excellent performance in many fields, including image recognition of a large amount of training data (such as ImageNet). In fact, hardware limitations and insufficient training data-sets are the challenges of getting high performance. Therefore, in this work the Deep Transfer Learning method using AlexNet pre-trained CNN is proposed to improve the performance of the face-recognition system even for a smaller number of images. The transfer learning method is used to fine-tuning on the last layer of AlexNet CNN model for new classification tasks. The data augmentation (DA) technique also proposed to minimize the over-fitting problem during Deep transfer learning training and to improve accuracy. The results proved the improvement in over-fitting and in performance after using the data augmentation technique. All the experiments were tested on UTeMFD, GTFD, and CASIA-Face V5 small data-sets. As a result, the proposed system achieved a high accuracy as 100% on UTeMFD, 96.67% on GTFD, and 95.60% on CASIA-Face V5 in less than 0.05 seconds of recognition time.

scholarly journals An Improved Local Binary Patterns Histograms Technique for Face Recognition for Real Time Applications

2019 ◽  
Vol 8 (2S7) ◽  
pp. 524-529 ◽  
Keyword(s):  
Face Recognition ◽  
Local Binary Patterns ◽  
Recognition System ◽  
Facial Features ◽  
Cascade Classifier ◽  
The Past ◽  
Face Recognition System ◽  
Unique System ◽  
Haar Cascade ◽  
Python Programming

Recently, face recognition and its applications has been considered as one of the image analysis most successful applications, especially over the past several years. Face Recognition is a unique system that can be used by using unique facial features for identification or verification of a person from a digital image. In a face recognition system, there are many technique that can be used. This paper provides an efficient of the Local Binary Patterns Histograms (LBPH) based technique provided by OpenCV library which is implemented in Python programming language which is well suitable for realistic scenarios. In this paper we also provide visual qualitative outcome with existing algorithm (Haar-cascade classifier and Local Binary Patterns Histograms (LBPH)). As a result, the proposed technique outperform better in terms of visual qualitative analysis.

Face recognition system method-based low light deep learning

2021 ◽  
Author(s):  
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Block Diagram ◽  
Recognition System ◽  
Combined Method ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
System Method ◽  
Comparison Results ◽  
Conversion Performance

The article presents a combined method for solving the problem of generative recognition of face. The block diagram of the algorithm for the proposed combined method is constructed. A modified approach based on the basic CycleGAN model to solving the problem of transforming domain of an image is proposed and an analysis of the conversion performance results is carried out. Evaluation of the effectiveness of the proposed algorithm under various conditions is carried out. The comparison results of the proposed approach with other modern methods are shown. Keywords transformation of images; generative adversarial network; face recognition; deep learning

scholarly journals Design of face recognition system based on CNN

2020 ◽  
Vol 1601 ◽  
pp. 052011
Author(s):  
Yong Li ◽  
Zhe Wang ◽  
Yang Li ◽  
Xu Zhao ◽  
Hanwen Huang
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Face Recognition System
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