Abstract
Noise, corruptions and variations in face images can seriously hurt the performance of face recognition systems. To make such systems robust, multiclass neuralnetwork classifiers capable of learning from noisy data have been suggested. However on large face data sets such systems cannot provide the robustness at a high level. In this paper we explore a pairwise neural-network system as an alternative approach to improving the robustness of face recognition. In our experiments this approach is shown to outperform the multiclass neural-network system in terms of the predictive accuracy on the face images corrupted by noise.
Highlights
The performance of face-recognition systems is achieved at a high level when these systems are robust to noise, corruptions, and variations in face images [1]
On large face image datasets, containing many images per class or large number of classes, such neural-network systems cannot provide the performance at a high level
Pairwise classification system transforms a multiclass problem into a set of binary classification problems for which class boundaries become much simpler than those for a multiclass system
Summary
The performance of face-recognition systems is achieved at a high level when these systems are robust to noise, corruptions, and variations in face images [1]. On large face image datasets, containing many images per class (subject) or large number of classes, such neural-network systems cannot provide the performance at a high level. This happens because boundaries between classes become complex and a recognition system can fail to solve a problem; see [1,2,3]. We can treat the outcomes of pairwise classifiers as class membership values (not as probabilities) and combine them to make decisions by using the winner-take-all strategy We found that this strategy can be efficiently implemented within a neural network paradigm in the competitive layer as described in [5].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
