Robust Algorithms for Real-World Face Recognition

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A practical face verification system is expected to achieve a False Accept Rate of 0.01% or lower whileensuring a False Reject Rate (FRR) of 5% or lower. In recent years, face recognition algorithms based on deepneural networks have achieved high accuracy when tested on face recognition database. However, when putinto real-world application, those algorithms are often not robust enough, due to factors such as differentlighting conditions, camera distances, face orientations, and occlusions.As a startup company, AltumView Systems Inc. has been developing various intelligent camera products sinceJanuary 2015. In this project, the university team will work with AltumView to improve the performance androbustness of its existing face recognition products. Research to be conducted includes: 1) try differentconfigurations of the cost functions of the deep neural network scheme, and identify the most effective way; 2)design new neural network architecture that is robust to different lighting conditions, camera distances, andface angles etc. between the ID photos and wild input photos; 3) design schemes to deal with partially occludedfaces caused by e.g., glasses; 4) investigate Generative Adversarial Networks for preprocessing of input photosand domain adaptation; and 5) investigate how to combine different techniques to improve the overallperformance of the system.