Department or Administrative Unit
Recent advances in the face detection (FD) and recognition (FR) technology may give an impression that the problem of face matching is essentially solved, e.g. via deep learning models using thousands of samples per face for training and validation on the available benchmark data-sets. Human vision system seems to handle face localization and matching problem differently from the modern FR systems, since humans detect faces instantly even in most cluttered environments, and often require a single view of a face to reliably distinguish it from all others. This prompted us to take a biologically inspired look at building a cognitive architecture that uses artificial neural nets at the face detection stage and adapts a single image per person (SIPP) approach for face image matching.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.
Borovikov, E., Vajda, S., Bonifant, M., & Gill, M. (2018). Looking at faces in the wild. Procedia Computer Science, 123, 104–109. https://doi.org/10.1016/j.procs.2018.01.017
Procedia Computer Science
© 2018 The Authors
This article was originally published Open Access in Procedia Computer Science. The full-text article from the publisher can be found here.