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Semi-supervised Learning with Constraints for Person Identification in Multimedia Data

2019-12-11 | |  91 |   48 |   0

Abstract

We address the problem of person identifification in TV series. We propose a unifified learning framework for multiclass classifification which incorporates labeled and unlabeled data, and constraints between pairs of features in the training. We apply the framework to train multinomial logistic regression classififiers for multi-class face recognition. The method is completely automatic, as the labeled data is obtained by tagging speaking faces using subtitles and fan transcripts of the videos. We demonstrate our approach on six episodes each of two diverse TV series and achieve state-of-the-art performance

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