Reference number : GC-2017-Q3-00423, amendment number : , agreement type : G, recipient legal name : Gong, Minglun (Memorial University of Newfoundland), recipient country : CA, recipient province : nan, recipient city : Newfoundland and Labrador|Terre-Neuve-et-Labrador, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : A practical face verification system is expected to achieve a False Accept Rate of 0.01% or lower while_x000D_
ensuring a False Reject Rate (FRR) of 5% or lower. In recent years, face recognition algorithms based on deep_x000D_
neural networks have achieved high accuracy when tested on face recognition database. However, when put_x000D_
into real-world application, those algorithms are often not robust enough, due to factors such as different_x000D_
lighting conditions, camera distances, face orientations, and occlusions._x000D_
As a startup company, AltumView Systems Inc. has been developing various intelligent camera products since_x000D_
January 2015. In this project, the university team will work with AltumView to improve the performance and_x000D_
robustness of its existing face recognition products. Research to be conducted includes: 1) try different_x000D_
configurations of the cost functions of the deep neural network scheme, and identify the most effective way; 2)_x000D_
design new neural network architecture that is robust to different lighting conditions, camera distances, and_x000D_
face angles etc. between the ID photos and wild input photos; 3) design schemes to deal with partially occluded_x000D_
faces caused by e.g., glasses; 4) investigate Generative Adversarial Networks for preprocessing of input photos_x000D_
and domain adaptation; and 5) investigate how to combine different techniques to improve the overall_x000D_
performance of the system., program purpose french : A practical face verification system is expected to achieve a False Accept Rate of 0.01% or lower while_x000D_
ensuring a False Reject Rate (FRR) of 5% or lower. In recent years, face recognition algorithms based on deep_x000D_
neural networks have achieved high accuracy when tested on face recognition database. However, when put_x000D_
into real-world application, those algorithms are often not robust enough, due to factors such as different_x000D_
lighting conditions, camera distances, face orientations, and occlusions._x000D_
As a startup company, AltumView Systems Inc. has been developing various intelligent camera products since_x000D_
January 2015. In this project, the university team will work with AltumView to improve the performance and_x000D_
robustness of its existing face recognition products. Research to be conducted includes: 1) try different_x000D_
configurations of the cost functions of the deep neural network scheme, and identify the most effective way; 2)_x000D_
design new neural network architecture that is robust to different lighting conditions, camera distances, and_x000D_
face angles etc. between the ID photos and wild input photos; 3) design schemes to deal with partially occluded_x000D_
faces caused by e.g., glasses; 4) investigate Generative Adversarial Networks for preprocessing of input photos_x000D_
and domain adaptation; and 5) investigate how to combine different techniques to improve the overall_x000D_
performance of the system., agreement title english : Robust Algorithms for Real-World Face Recognition, agreement title french : Robust Algorithms for Real-World Face Recognition, agreement number : EGP, agreement value : 25000.0, agreement start date : 2017-10-18, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada