Down-Sampling Face Images and Low-Resolution Face Recognition

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.

Download Full-text

Adapted discriminative coupled mappings for low-resolution face recognition with one-shot

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-212454 ◽

2021 ◽

pp. 1-15

Author(s):

Yongjie Chu ◽

Touqeer Ahmad ◽

Lindu Zhao

Keyword(s):

Face Recognition ◽

Law Enforcement ◽

High Resolution ◽

Domain Adaptation ◽

Face Image ◽

Discriminative Learning ◽

Low Resolution ◽

Face Images ◽

World Environment

Low-resolution face recognition with one-shot is a prevalent problem encountered in law enforcement, where it generally requires to recognize the low-resolution face images captured by surveillance cameras with the only one high-resolution profile face image in the database. The problem is very tough because the available samples is quite few and the quality of unknown images is quite low. To effectively address this issue, this paper proposes Adapted Discriminative Coupled Mappings (AdaDCM) approach, which integrates domain adaptation and discriminative learning. To achieve good domain adaptation performance for small size dataset, a new domain adaptation technique called Bidirectional Locality Matching-based Domain Adaptation (BLM-DA) is first developed. Then the proposed AdaDCM is formulated by unifying BLM-DA and discriminative coupled mappings into a single framework. AdaDCM is extensively evaluated on FERET, LFW, and SCface databases, which includes LR face images obtained in constrained, unconstrained, and real-world environment. The promising results on these datasets demonstrate the effectiveness of AdaDCM in LR face recognition with one-shot.

Download Full-text

Kernel Coupled Cross-Regression for Low-Resolution Face Recognition

Mathematical Problems in Engineering ◽

10.1155/2013/153790 ◽

2013 ◽

Vol 2013 ◽

pp. 1-9

Author(s):

Zhifei Wang ◽

Zhenjiang Miao ◽

Yanli Wan ◽

Zhen Tang

Keyword(s):

Face Recognition ◽

Time Complexity ◽

Recognition Performance ◽

Recognition Rate ◽

Feature Space ◽

Low Resolution ◽

Face Images ◽

Novel Method ◽

Nonlinear Embedding ◽

Spectral Regression

Low resolution (LR) in face recognition (FR) surveillance applications will cause the problem of dimensional mismatch between LR image and its high-resolution (HR) template. In this paper, a novel method called kernel coupled cross-regression (KCCR) is proposed to deal with this problem. Instead of processing in the original observing space directly, KCCR projects LR and HR face images into a unified nonlinear embedding feature space using kernel coupled mappings and graph embedding. Spectral regression is further employed to improve the generalization performance and reduce the time complexity. Meanwhile, cross-regression is developed to fully utilize the HR embedding to increase the information of the LR space, thus to improve the recognition performance. Experiments on the FERET and CMU PIE face database show that KCCR outperforms the existing structure-based methods in terms of recognition rate as well as time complexity.

Download Full-text

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

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691319400022 ◽

2019 ◽

Vol 17 (02) ◽

pp. 1940002 ◽

Cited By ~ 1

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.

Download Full-text

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

Graduate Research in Engineering and Technology ◽

10.47893/gret.2013.1032 ◽

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.

Download Full-text

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

Journal of Experimental Psychology Applied ◽

10.1037/xap0000180 ◽

2019 ◽

Vol 25 (2) ◽

pp. 256-279 ◽

Cited By ~ 3

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

Download Full-text

Some Information Geometric Aspects of Cyber Security by Face Recognition

Entropy ◽

10.3390/e23070878 ◽

2021 ◽

Vol 23 (7) ◽

pp. 878

Author(s):

C. T. J. Dodson ◽

John Soldera ◽

Jacob Scharcanski

Keyword(s):

Face Recognition ◽

Cyber Security ◽

Probability Distributions ◽

Geodesic Distance ◽

Recognition Method ◽

Frequency Distributions ◽

Face Images ◽

User Access ◽

Multidimensional Methods ◽

Joint Probabilities

Secure user access to devices and datasets is widely enabled by fingerprint or face recognition. Organization of the necessarily large secure digital object datasets, with objects having content that may consist of images, text, video or audio, involves efficient classification and feature retrieval processing. This usually will require multidimensional methods applicable to data that is represented through a family of probability distributions. Then information geometry is an appropriate context in which to provide for such analytic work, whether with maximum likelihood fitted distributions or empirical frequency distributions. The important provision is of a natural geometric measure structure on families of probability distributions by representing them as Riemannian manifolds. Then the distributions are points lying in this geometrical manifold, different features can be identified and dissimilarities computed, so that neighbourhoods of objects nearby a given example object can be constructed. This can reveal clustering and projections onto smaller eigen-subspaces which can make comparisons easier to interpret. Geodesic distances can be used as a natural dissimilarity metric applied over data described by probability distributions. Exploring this property, we propose a new face recognition method which scores dissimilarities between face images by multiplying geodesic distance approximations between 3-variate RGB Gaussians representative of colour face images, and also obtaining joint probabilities. The experimental results show that this new method is more successful in recognition rates than published comparative state-of-the-art methods.

Download Full-text

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

IEEE Access ◽

10.1109/access.2021.3080712 ◽

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

Download Full-text

Face recognition in an intelligent door lock with ResNet model based on deep learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189624 ◽

2021 ◽

pp. 1-11

Author(s):

Suphawimon Phawinee ◽

Jing-Fang Cai ◽

Zhe-Yu Guo ◽

Hao-Ze Zheng ◽

Guan-Chen Chen

Keyword(s):

Face Recognition ◽

Detection System ◽

Low Cost ◽

Ease Of Use ◽

Raspberry Pi ◽

Training Methods ◽

Graphics Recognition ◽

Face Images ◽

The Face ◽

Door Lock

Internet of Things is considerably increasing the levels of convenience at homes. The smart door lock is an entry product for smart homes. This work used Raspberry Pi, because of its low cost, as the main control board to apply face recognition technology to a door lock. The installation of the control sensing module with the GPIO expansion function of Raspberry Pi also improved the antitheft mechanism of the door lock. For ease of use, a mobile application (hereafter, app) was developed for users to upload their face images for processing. The app sends the images to Firebase and then the program downloads the images and captures the face as a training set. The face detection system was designed on the basis of machine learning and equipped with a Haar built-in OpenCV graphics recognition program. The system used four training methods: convolutional neural network, VGG-16, VGG-19, and ResNet50. After the training process, the program could recognize the user’s face to open the door lock. A prototype was constructed that could control the door lock and the antitheft system and stream real-time images from the camera to the app.

Download Full-text

Efficient Low-Resolution Face Recognition via Bridge Distillation

IEEE Transactions on Image Processing ◽

10.1109/tip.2020.2995049 ◽

2020 ◽

Vol 29 ◽

pp. 6898-6908

Author(s):

Shiming Ge ◽

Shengwei Zhao ◽

Chenyu Li ◽

Yu Zhang ◽

Jia Li

Keyword(s):

Face Recognition ◽

Low Resolution

Download Full-text