Super-resolution for high magnification face images

2007 ◽  
Author(s):  
Yi Yao ◽  
Besma Abidi ◽  
Nathan D. Kalka ◽  
Natalia Schmid ◽  
Mongi Abidi
Keyword(s):  
Super Resolution ◽  
High Magnification ◽  
Face Images

Low-resolution and open-set face recognition via recursive label propagation based on statistical classification

2019 ◽  
Vol 17 (02) ◽  
pp. 1940002 ◽  
Author(s):  
Shan Xue ◽  
Hong Zhu
Keyword(s):  
Face Recognition ◽  
Super Resolution ◽  
Recall Rate ◽  
Face Image ◽  
Label Propagation ◽  
Statistical Classification ◽  
Low Resolution ◽  
Face Images ◽  
Open Set ◽  
Discriminative Feature

In video surveillance, the captured face images are usually suffered from low-resolution (LR), besides, not all the probe images have mates in the gallery under the premise that only a single frontal high-resolution (HR) face image per subject. To address this problem, a novel face recognition framework called recursive label propagation based on statistical classification (ReLPBSC) has been proposed in this paper. Firstly, we employ VGG to extract robust discriminative feature vectors to represent each face. Then we select the corresponding LR face in the probe for each HR gallery face by similarity. Based on the picked HR–LR pairs, ReLPBSC is implemented for recognition. The main contributions of the proposed approach are as follows: (i) Inspired by substantial achievements of deep learning methods, VGG is adopted to achieve discriminative representation for LR faces to avoid the super-resolution steps; (ii) the accepted and rejected threshold parameters, which are not fixed in face recognition, can be achieved with ReLPBSC adaptively; (iii) the unreliable subjects never enrolled in the gallery can be rejected automatically with designed methods. Experimental results in [Formula: see text] pixels resolution show that the proposed method can achieve 86.64% recall rate while keeping 100% precision.

ComSupResNet: A Compact Super-Resolution Network for Low-Resolution Face Images

2020 ◽  
Author(s):  
Aashish Rai ◽  
Vishal Chudasama ◽  
Kishor Upla ◽  
Kiran Raja ◽  
Raghavendra Ramachandra ◽  
...  
Keyword(s):  
Super Resolution ◽  
Low Resolution ◽  
Face Images

Super-Resolution Restoration of Low Quality Face Images

2018 ◽  
Vol 55 (3) ◽  
pp. 031007
Author(s):  
唐佳林 Tang Jialin ◽  
陈泽彬 Chen Zebin ◽  
苏秉华 Su Binghua ◽  
李克勤 Li Keqin
Keyword(s):  
Super Resolution ◽  
Face Images

scholarly journals A New Integrated Approach Based on the Iterative Super-Resolution Algorithm and Expectation Maximization for Face Hallucination

2020 ◽  
Vol 10 (2) ◽  
pp. 718 ◽  
Author(s):  
K. Lakshminarayanan ◽  
R. Santhana Krishnan ◽  
E. Golden Julie ◽  
Y. Harold Robinson ◽  
Raghvendra Kumar ◽  
...  
Keyword(s):  
High Resolution ◽  
Sparse Representation ◽  
Expectation Maximization ◽  
Super Resolution ◽  
Integrated Approach ◽  
Face Hallucination ◽  
Resolution Image ◽  
High Resolution Image ◽  
Face Images ◽  
Resolution Algorithm

This paper proposed and verified a new integrated approach based on the iterative super-resolution algorithm and expectation-maximization for face hallucination, which is a process of converting a low-resolution face image to a high-resolution image. The current sparse representation for super resolving generic image patches is not suitable for global face images due to its lower accuracy and time-consumption. To solve this, in the new method, training global face sparse representation was used to reconstruct images with misalignment variations after the local geometric co-occurrence matrix. In the testing phase, we proposed a hybrid method, which is a combination of the sparse global representation and the local linear regression using the Expectation Maximization (EM) algorithm. Therefore, this work recovered the high-resolution image of a corresponding low-resolution image. Experimental validation suggested improvement of the overall accuracy of the proposed method with fast identification of high-resolution face images without misalignment.

scholarly journals AN EFFICIENT METHOD TO FEED HIGH RESOLUTION IMAGES TO FACIAL ANALYSIS SYSTEMS

2013 ◽  
pp. 68-71
Author(s):  
ROOPA R ◽  
MRS. VANI.K. S ◽  
MRS. NAGAVENI. V
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
High Resolution ◽  
Super Resolution ◽  
Poor Quality ◽  
Low Resolution ◽  
Resolution Method ◽  
Facial Analysis ◽  
Face Images ◽  
High Resolution Images

Image Processing is any form of signal processing for which the image is an input such as a photograph or video frame. The output of image processing may be either an image or a set of characteristics or parameters related to the image. In many facial analysis systems like Face Recognition face is used as an important biometric. Facial analysis systems need High Resolution images for their processing. The video obtained from inexpensive surveillance cameras are of poor quality. Processing of poor quality images leads to unexpected results. To detect face images from a video captured by inexpensive surveillance cameras, we will use AdaBoost algorithm. If we feed those detected face images having low resolution and low quality to face recognition systems they will produce some unstable and erroneous results. Because these systems have problem working with low resolution images. Hence we need a method to bridge the gap between on one hand low- resolution and low-quality images and on the other hand facial analysis systems. Our approach is to use a Reconstruction Based Super Resolution method. In Reconstruction Based Super Resolution method we will generate a face-log containing images of similar frontal faces of the highest possible quality using head pose estimation technique. Then, we use a Learning Based Super-Resolution algorithm applied to the result of the reconstruction-based part to improve the quality by another factor of two. Hence the total system quality factor will be improved by four.

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