LIFT: A new framework of learning from testing data for face recognition
Document Type
Article
Date of Original Version
2-15-2011
Abstract
In this paper, a novel learning methodology for face recognition, LearnIng From Testing data (LIFT) framework, is proposed. Considering many face recognition problems featured by the inadequate training examples and availability of the vast testing examples, we aim to explore the useful information from the testing data to facilitate learning. The one-against-all technique is integrated into the learning system to recover the labels of the testing data, and then expand the training population by such recovered data. In this paper, neural networks and support vector machines are used as the base learning models. Furthermore, we integrate two other transductive methods, consistency method and LRGA method into the LIFT framework. Experimental results and various hypothesis testing over five popular face benchmarks illustrate the effectiveness of the proposed framework. © 2010 Elsevier B.V.
Publication Title, e.g., Journal
Neurocomputing
Volume
74
Issue
6
Citation/Publisher Attribution
Cao, Yuan, Haibo He, and He (Helen) Huang. "LIFT: A new framework of learning from testing data for face recognition." Neurocomputing 74, 6 (2011): 916-929. doi: 10.1016/j.neucom.2010.10.015.