Face Recognition in Poor-Quality Video: Evidence From Security Surveillance

1999 ◽  
Vol 10 (3) ◽  
pp. 243-248 ◽  
Author(s):  
A. Mike Burton ◽  
Stephen Wilson ◽  
Michelle Cowan ◽  
Vicki Bruce
Keyword(s):  
Face Recognition ◽  
Poor Quality ◽  
Quality Video ◽  
Security Surveillance

scholarly journals InnovFaceNet: Deep Face Recognition for Industrial Environments

2021 ◽  
Vol 6 (1) ◽  
pp. 1-4
Author(s):  
Nagarjun Gururaj ◽  
Kanika Batra
Keyword(s):  
Face Recognition ◽  
Criminal Justice ◽  
Real Time ◽  
Intelligent Systems ◽  
Facial Recognition ◽  
Industrial Plant ◽  
The World ◽  
Industrial Environments ◽  
Or Organization ◽  
Security Surveillance

In recent times the usage of intelligent systems have paved way formany applications to be robust and self-reliant. One such popularand vast growing technology is face recognition. Facial Recognitiontechnology is used in security, surveillance, criminal justice systemsand many other multimedia platforms. This work proposes a realtime facial recognition technology which can be used in any industrialsetup eliminating manual supervision, ensuring authorized accessto the personnel in the plant. Due to the recent development ofCOVID-19 pandemic around the world, wearing masks has becomea necessity. Our proposed facial recognition technology identifies aperson’s face with mask or no mask in real time with a speed of20 FPS on a CPU and an F1-score of 95.07%. This makes ouralgorithm fast, secure, robust and deployable on a simple personalcomputer or any edge device at any industrial plant or organization.

scholarly journals Using Standardized Videos to Examine the Validity of the Shared Decision Making Process Scale: Results of a Randomized Online Experiment

2021 ◽  
pp. 0272989X2110292
Author(s):  
K. D. Valentine ◽  
Brittney Mancini ◽  
Ha Vo ◽  
Suzanne Brodney ◽  
Carol Cosenza ◽  
...  
Keyword(s):  
Decision Making ◽  
Individual Differences ◽  
Cancer Screening ◽  
Shared Decision Making ◽  
Poor Quality ◽  
Shared Decision ◽  
High Cholesterol ◽  
Hypothetical Scenario ◽  
Quality Video ◽  
Process Scale

Background The Shared Decision Making (SDM) Process scale is a brief, patient-reported measure of SDM with demonstrated validity in surgical decision making studies. Herein we examine the validity of the scores in assessing SDM for cancer screening and medication decisions through standardized videos of good-quality and poor-quality SDM consultations. Method An online sample was randomized to a clinical decision—colon cancer screening or high cholesterol—and a viewing order—good-quality video first or poor-quality video first. Participants watched both videos, completing a survey after each video. Surveys included the SDM Process scale and the 9-item SDM Questionnaire (SDM-Q-9); higher scores indicated greater SDM. Multilevel linear regressions identified if video, order, or their interaction predicted SDM Process scores. To identify how the SDM Process score classified videos, area under the curve (AUC) was calculated. The correlation between SDM Process score and SDM-Q-9 assessed construct validity. Heterogeneity analyses were conducted. Results In the sample of 388 participants (68% white, 70% female, average age 45 years) good-quality videos received higher SDM Process scores than poor-quality videos ( Ps < 0.001), and those who viewed the good-quality high cholesterol video first tended to rate the videos higher. SDM Process scores were related to SDM-Q-9 scores ( rs > 0.58; Ps < 0.001). AUC was poor (0.69) for the high cholesterol model and fair (0.79) for the colorectal cancer model. Heterogeneity analyses suggested individual differences were predictive of SDM Process scores. Conclusion SDM Process scores showed good evidence of validity in a hypothetical scenario but were lacking in ability to classify good-quality or poor-quality videos accurately. Considerable heterogeneity of scoring existed, suggesting that individual differences played a role in evaluating good- or poor-quality SDM conversations.

scholarly journals The Absence of Evidence is Evidence of Non-Sense: Cross-Sectional Study on the Quality of Psoriasis-Related Videos on YouTube and Their Reception by Health Seekers (Preprint)

2018 ◽  
Author(s):  
Simon M Mueller ◽  
Pierre Jungo ◽  
Lucian Cajacob ◽  
Simon Schwegler ◽  
Peter Itin ◽  
...  
Keyword(s):  
Health Information ◽  
Medical Information ◽  
Total Duration ◽  
Cross Sectional Study ◽  
Poor Quality ◽  
Cross Sectional ◽  
Global Quality ◽  
Quality Video ◽  
Depth Study

BACKGROUND Approximately 80% of internet users access health information online and patients with chronic illnesses especially rely on internet-based resources. YouTube ranks second among the most accessed websites worldwide and hosts an increasing number of videos with medical information. However, their quality is sometimes unscientific, misleading, or even harmful. OBJECTIVE As little is known about YouTube as a source of information on psoriasis, we aimed to investigate the quality of psoriasis-related videos and, if necessary, point out strategies for their improvement. METHODS The quality of the 100 most viewed psoriasis-related videos was assessed using the DISCERN instrument and the Global Quality Scale (GQS) by categorizing the videos into useful, misleading, and dangerous and by evaluating the reception of the videos by users. RESULTS Evaluation of the videos exhibited a total of 117,221,391 views and a total duration of 10:28 hour. The majority of clips contained anecdotal personal experiences with complementary and alternative psoriasis treatments, topical treatments, and nutrition and diets being the most frequently addressed topics. While advertisements accounted for 26.0% (26/100) of the videos, evidence-based health information amounted to only 20.0% (20/100); 32.0% (32/100) of the videos were classified as useful, 52.0% (52/100) as misleading, and 11.0% (11/100) as even dangerous. The quality of the videos evaluated by DISCERN and GQS was generally low (1.87 and 1.95, respectively, on a 1 to 5 scale with 5 being the maximum). Moreover, we found that viewers rated poor-quality videos better than higher quality videos. CONCLUSIONS Our in-depth study demonstrates that nearly two-thirds of the psoriasis-related videos we analyzed disseminate misleading or even dangerous content. Subjective anecdotal and unscientific content is disproportionately overrepresented and poor-quality videos are predominantly rated positively by users, while higher quality video clips receive less positive ratings. Strategies by professional dermatological organizations are urgently needed to improve the quality of information on psoriasis on YouTube and other social media.

scholarly journals Face Recognition in SSPP Problem Using Face Relighting Based on Coupled Bilinear Model

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 43 ◽  
Author(s):  
Sang-Il Choi ◽  
Yonggeol Lee ◽  
Minsik Lee
Keyword(s):  
Face Recognition ◽  
Feature Space ◽  
Training Image ◽  
Poor Quality ◽  
Input Image ◽  
Bilinear Model ◽  
Proposed Model ◽  
Virtual Images ◽  
Single Input

There have been decades of research on face recognition, and the performance of many state-of-the-art face recognition algorithms under well-conditioned environments has become saturated. Accordingly, recent research efforts have focused on difficult but practical challenges. One such issue is the single sample per person (SSPP) problem, i.e., the case where only one training image of each person. While this problem is challenging because it is difficult to establish the within-class variation, working toward its solution is very practical because often only a few images of a person are available. To address the SSPP problem, we propose an efficient coupled bilinear model that generates virtual images under various illuminations using a single input image. The proposed model is inspired by the knowledge that the illuminance of an image is not sensitive to the poor quality of a subspace-based model, and it has a strong correlation to the image itself. Accordingly, a coupled bilinear model was constructed that retrieves the illuminance information from an input image. This information is then combined with the input image to estimate the texture information, from which we can generate virtual illumination conditions. The proposed method can instantly generate numerous virtual images of good quality, and these images can then be utilized to train the feature space for resolving SSPP problems. Experimental results show that the proposed method outperforms the existing algorithms.

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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