Paper Info
Title
Human-Machine Collaboration For Face Recognition
Abstract
Despite advances in deep learning and facial recognition techniques, the problem of fault-intolerant facial recognition remains challenging. With the current state of progress in the field of automatic face recognition and the in-feasibility of fully manual recognition, the situation calls for human-machine collaborative methods. We design a system that uses machine predictions for a given face to generate queries that are answered by human experts to provide the system with the information required to predict the identity of the face correctly. We use a Markov Decision Process for which we devise an appropriate query structure and a reward structure to generate these queries in a budget or accuracy-constrained setting. Finally, as we do not know the capabilities of the human experts involved, we model each human as a bandit and adopt a multi-armed bandit approach with consensus queries to efficiently estimate their individual accuracies, enabling us to maximize the accuracy of our system. Through careful analysis and experimentation on real-world data-sets using humans, we show that our system outperforms methods that exploit only machine intelligence, simultaneously being highly cost-efficient as compared to fully manual methods. In summary, our system uses human-machine collaboration for face recognition problem more intelligently and efficiently.
Year
DOI
Venue
2020
10.1145/3371158.3371160
PROCEEDINGS OF THE 7TH ACM IKDD CODS AND 25TH COMAD (CODS-COMAD 2020)
Keywords
Field
DocType
facial recognition techniques, Markov Decision Process, crowdsourcing, multi-armed bandits
Facial recognition system,Human–machine system,Computer science,Human–computer interaction
Conference
Citations 
PageRank 
References 
0
0.34
21
Authors
6
Name
Order
Citations
PageRank
Saurabh Ravindranath100.34
Rahul Baburaj200.34
Vineeth Nallure Balasubramanian326536.44
NageswaraRao Namburu400.34
Sujit Gujar57625.33
C. V. Jawahar61700148.58