FaceCognize: An approach to Face Recognition for low resolution image

2021 ◽  
Author(s):  
Naeem Patel ◽  
Yashveer Singh Sohi ◽  
Sai Reddy
Keyword(s):  
Face Recognition ◽  
Low Resolution ◽  
Resolution Image

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 %.

Fractional Bat and Multi-Kernel-Based Spherical SVM for Low Resolution Face Recognition

2017 ◽  
Vol 31 (08) ◽  
pp. 1756014 ◽  
Author(s):  
Renjith Thomas ◽  
M. J. S. Rangachar
Keyword(s):  
Face Recognition ◽  
Gabor Filter ◽  
Super Resolution ◽  
Bat Algorithm ◽  
Training Data ◽  
Distinct Advantage ◽  
Svm Classifier ◽  
Low Resolution ◽  
Resolution Image ◽  
And Performance

Face recognition is an important aspect of the biometric surveillance system. Generally, face recognition is a type of biometric system that can identify a specific individual by analyzing and comparing patterns in the facial image. Face recognition has distinct advantage over other biometrics is noncontact process. It has a wide variety of applications in both the law enforcement and nonlaw enforcement. While using the low resolution face images, the resolution of the image gets degraded. In this paper, to enhance the performance rate for low resolution image, the fractional Bat algorithm and multi-kernel-based spherical SVM classifier is proposed. Initially, the low resolution image is converted into the high resolution images by the kernel regression method. The GWTM process is utilized for the feature extraction by the Gabor filter, wavelet transform and local binary pattern (texture descriptors). Then, the super resolution images are applied to the feature level fusion by using the fractional Bat algorithm which comprises of fractional theory and Bat algorithm. Finally, the multi-kernel-based spherical SVM classifier is introduced for the recognition of feature images. The experimental results and performance analysis evaluated by the comparison metrics are FAR, FRR and Accuracy with existing systems. Thus, the outcome of our proposed system achieves the highest accuracy of 95% based on the training data samples, stopping criterion and number of draw attempts.

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 %.

Caricaturing as a general method to improve poor face recognition: Evidence from low-resolution images, other-race faces, and older adults.

2019 ◽  
Vol 25 (2) ◽  
pp. 256-279 ◽  
Author(s):  
Amy Dawel ◽  
Tsz Ying Wong ◽  
Jodie McMorrow ◽  
Callin Ivanovici ◽  
Xuming He ◽  
...  
Keyword(s):  
Older Adults ◽  
Face Recognition ◽  
Low Resolution ◽  
General Method ◽  
Low Resolution Images

scholarly journals Fast Target Localization Method for FMCW MIMO Radar via VDSR Neural Network

2021 ◽  
Vol 13 (10) ◽  
pp. 1956
Author(s):  
Jingyu Cong ◽  
Xianpeng Wang ◽  
Xiang Lan ◽  
Mengxing Huang ◽  
Liangtian Wan
Keyword(s):  
Neural Network ◽  
Continuous Wave ◽  
Super Resolution ◽  
Mimo Radar ◽  
Target Localization ◽  
Estimation Accuracy ◽  
Low Resolution ◽  
Range Estimation ◽  
Resolution Image ◽  
Resolution Imaging

The traditional frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar two-dimensional (2D) super-resolution (SR) estimation algorithm for target localization has high computational complexity, which runs counter to the increasing demand for real-time radar imaging. In this paper, a fast joint direction-of-arrival (DOA) and range estimation framework for target localization is proposed; it utilizes a very deep super-resolution (VDSR) neural network (NN) framework to accelerate the imaging process while ensuring estimation accuracy. Firstly, we propose a fast low-resolution imaging algorithm based on the Nystrom method. The approximate signal subspace matrix is obtained from partial data, and low-resolution imaging is performed on a low-density grid. Then, the bicubic interpolation algorithm is used to expand the low-resolution image to the desired dimensions. Next, the deep SR network is used to obtain the high-resolution image, and the final joint DOA and range estimation is achieved based on the reconstructed image. Simulations and experiments were carried out to validate the computational efficiency and effectiveness of the proposed framework.

scholarly journals A Study on the Performance of Unconstrained Very Low Resolution Face Recognition: Analyzing Current Trends and New Research Directions

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Luis S. Luevano ◽  
Leonardo Chang ◽  
Heydi Mendez-Vazquez ◽  
Yoanna Martinez-Diaz ◽  
Miguel Gonzalez-Mendoza
Keyword(s):  
Face Recognition ◽  
Low Resolution ◽  
Research Directions ◽  
Current Trends ◽  
New Research

scholarly journals Multimodal Low Resolution Face and Frontal Gait Recognition from Surveillance Video

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1013
Author(s):  
Sayan Maity ◽  
Mohamed Abdel-Mottaleb ◽  
Shihab S. Asfour
Keyword(s):  
Face Recognition ◽  
High Resolution ◽  
Gait Cycle ◽  
Gait Recognition ◽  
Recognition System ◽  
Multimodal Fusion ◽  
Surveillance Video ◽  
Low Resolution ◽  
Model Free ◽  
Face Images

Biometric identification using surveillance video has attracted the attention of many researchers as it can be applicable not only for robust identification but also personalized activity monitoring. In this paper, we present a novel multimodal recognition system that extracts frontal gait and low-resolution face images from frontal walking surveillance video clips to perform efficient biometric recognition. The proposed study addresses two important issues in surveillance video that did not receive appropriate attention in the past. First, it consolidates the model-free and model-based gait feature extraction approaches to perform robust gait recognition only using the frontal view. Second, it uses a low-resolution face recognition approach which can be trained and tested using low-resolution face information. This eliminates the need for obtaining high-resolution face images to create the gallery, which is required in the majority of low-resolution face recognition techniques. Moreover, the classification accuracy on high-resolution face images is considerably higher. Previous studies on frontal gait recognition incorporate assumptions to approximate the average gait cycle. However, we quantify the gait cycle precisely for each subject using only the frontal gait information. The approaches available in the literature use the high resolution images obtained in a controlled environment to train the recognition system. However, in our proposed system we train the recognition algorithm using the low-resolution face images captured in the unconstrained environment. The proposed system has two components, one is responsible for performing frontal gait recognition and one is responsible for low-resolution face recognition. Later, score level fusion is performed to fuse the results of the frontal gait recognition and the low-resolution face recognition. Experiments conducted on the Face and Ocular Challenge Series (FOCS) dataset resulted in a 93.5% Rank-1 for frontal gait recognition and 82.92% Rank-1 for low-resolution face recognition, respectively. The score level multimodal fusion resulted in 95.9% Rank-1 recognition, which demonstrates the superiority and robustness of the proposed approach.

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