Multiple experts for robust face authentication

1998 ◽  
Author(s):  
Stephane Pigeon ◽  
Luc Vandendorpe
Keyword(s):  
Face Authentication ◽  
Multiple Experts

Robust Human Face Authentication Leveraging Acoustic Sensing on Smartphones

2021 ◽  
pp. 1-1
Author(s):  
Bing Zhou ◽  
Zongxing Xie ◽  
Yinuo Zhang ◽  
Jay Lohokare ◽  
Ruipeng Gao ◽  
...  
Keyword(s):  
Human Face ◽  
Acoustic Sensing ◽  
Face Authentication

scholarly journals Dealing with multiple experts and non-stationarity in inverse reinforcement learning: an application to real-life problems

2021 ◽  
Author(s):  
Amarildo Likmeta ◽  
Alberto Maria Metelli ◽  
Giorgia Ramponi ◽  
Andrea Tirinzoni ◽  
Matteo Giuliani ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Real World ◽  
Real Life ◽  
User Preferences ◽  
Inverse Reinforcement Learning ◽  
Water Release ◽  
Reward Function ◽  
Model Free ◽  
Conflicting Objectives ◽  
Multiple Experts

AbstractIn real-world applications, inferring the intentions of expert agents (e.g., human operators) can be fundamental to understand how possibly conflicting objectives are managed, helping to interpret the demonstrated behavior. In this paper, we discuss how inverse reinforcement learning (IRL) can be employed to retrieve the reward function implicitly optimized by expert agents acting in real applications. Scaling IRL to real-world cases has proved challenging as typically only a fixed dataset of demonstrations is available and further interactions with the environment are not allowed. For this reason, we resort to a class of truly batch model-free IRL algorithms and we present three application scenarios: (1) the high-level decision-making problem in the highway driving scenario, and (2) inferring the user preferences in a social network (Twitter), and (3) the management of the water release in the Como Lake. For each of these scenarios, we provide formalization, experiments and a discussion to interpret the obtained results.

Personalized Face Authentication Based On Few-Shot Meta-Learning

2021 ◽  
Author(s):  
Chaehun Shin ◽  
Jangho Lee ◽  
Byunggook Na ◽  
Sungroh Yoon
Keyword(s):  
Face Authentication ◽  
Meta Learning

scholarly journals Do We Need Depth in State-Of-The-Art Face Authentication?

2020 ◽  
Author(s):  
Amir Livne ◽  
Ziv Aviv ◽  
Shahaf Grofit ◽  
Alex Bronstein ◽  
Ron Kimmel
Keyword(s):  
State Of The Art ◽  
Face Authentication

scholarly journals A Mathematical Framework for Combining Decisions of Multiple Experts toward Accurate and Remote Diagnosis of Malaria Using Tele-Microscopy

PLoS ONE ◽  
2012 ◽  
Vol 7 (10) ◽  
pp. e46192 ◽  
Author(s):  
Sam Mavandadi ◽  
Steve Feng ◽  
Frank Yu ◽  
Stoyan Dimitrov ◽  
Karin Nielsen-Saines ◽  
...  
Keyword(s):  
Mathematical Framework ◽  
Remote Diagnosis ◽  
Multiple Experts

scholarly journals Constructing Benchmark Databases and Protocols for Medical Image Analysis: Diabetic Retinopathy

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Tomi Kauppi ◽  
Joni-Kristian Kämäräinen ◽  
Lasse Lensu ◽  
Valentina Kalesnykiene ◽  
Iiris Sorri ◽  
...  
Keyword(s):  
Image Analysis ◽  
Diabetic Retinopathy ◽  
Medical Image ◽  
Image Annotation ◽  
Medical Image Analysis ◽  
Software Tool ◽  
Ground Truth ◽  
Spatial Span ◽  
Benchmark Database ◽  
Multiple Experts

We address the performance evaluation practices for developing medical image analysis methods, in particular, how to establish and share databases of medical images with verified ground truth and solid evaluation protocols. Such databases support the development of better algorithms, execution of profound method comparisons, and, consequently, technology transfer from research laboratories to clinical practice. For this purpose, we propose a framework consisting of reusable methods and tools for the laborious task of constructing a benchmark database. We provide a software tool for medical image annotation helping to collect class label, spatial span, and expert's confidence on lesions and a method to appropriately combine the manual segmentations from multiple experts. The tool and all necessary functionality for method evaluation are provided as public software packages. As a case study, we utilized the framework and tools to establish the DiaRetDB1 V2.1 database for benchmarking diabetic retinopathy detection algorithms. The database contains a set of retinal images, ground truth based on information from multiple experts, and a baseline algorithm for the detection of retinopathy lesions.

Active vision-based face authentication

2000 ◽  
Vol 18 (4) ◽  
pp. 299-314 ◽  
Author(s):  
M Tistarelli ◽  
E Grosso
Keyword(s):  
Active Vision ◽  
Face Authentication

Supervised learning from multiple experts

2009 ◽  
Author(s):  
Vikas C. Raykar ◽  
Shipeng Yu ◽  
Linda H. Zhao ◽  
Anna Jerebko ◽  
Charles Florin ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Multiple Experts

scholarly journals The Formulation of Epidemic Prevention Work of COVID-19 for Colleges and Universities: Priorities and Recommendations

2021 ◽  
Vol 13 (4) ◽  
pp. 2081
Author(s):  
Wan-Chi Jackie Hsu ◽  
Huai-Wei Lo ◽  
Chin-Cheng Yang
Keyword(s):  
Disease Transmission ◽  
Colleges And Universities ◽  
College Campuses ◽  
Prevention Measures ◽  
Campus Environment ◽  
Overseas Students ◽  
Entry Points ◽  
The World ◽  
Criteria Weights ◽  
Multiple Experts

As the Coronavirus disease 2019 (COVID-19) epidemic spreads all over the world, governments of various countries are actively adopting epidemic prevention measures to curb the spread of the disease. However, colleges and universities are one of the most likely places for cluster infections. The main reason is that college students have frequent social activities, and many students come from different countries, which may very likely cause college campuses to be entry points of disease transmission. Therefore, this study proposes a framework of epidemic prevention work, and further explores the importance and priority of epidemic prevention works. First of all, 32 persons in charge of epidemic prevention from various universities in Taiwan were invited to jointly formulate a campus epidemic prevention framework and determined 5 dimensions and 36 epidemic prevention works/measures/criteria. Next, Bayesian best worst method (BWM) was used to generate a set of optimal group criteria weights. This method can not only integrate the opinions of multiple experts, but also effectively reduce the complexity of expert interviews to obtain more reliable results. The results show that the five most important measures for campus epidemic prevention are the establishment of a campus epidemic prevention organization, comprehensive disinfection of the campus environment, maintenance of indoor ventilation, proper isolation of contacts with confirmed cases, and management of immigration regulations for overseas students. This study provides colleges and universities around the world to formulate anti-epidemic measures to effectively reduce the probability of COVID-19 transmission on campuses to protect students’ right to education.

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