Who's that Actor? Automatic Labelling of Actors in TV Series Starting from IMDB Images

By Rahaf Aljundi, Punarjay Chakravarty, Tinne Tuytelaars. 2016

13 citations recorded by the source corpus.

In this work, we aim at automatically labeling actors in a TV series. Rather than relying on transcripts and subtitles, as has been demonstrated in the past, we show how to achieve this goal starting from a set of example images of each of the main actors involved, collected from the Internet Movie Database (IMDB). The problem then becomes one of domain adaptation: actors’ IMDB photos are typically taken at awards ceremonies and are quite different from their appearances in TV series. In each series as well, there is considerable change in actor appearance due to makeup, lighting, ageing, etc. To bridge this gap, we propose a graph-matching based self-labelling algorithm, which we coin HSL (Hungarian Self Labeling). Further, we propose a new metric to be used in this context, as well as an extension that is more robust to outliers, where prototypical faces for each of the actors are selected based on a hierarchical clustering procedure. We conduct experiments with 15 episodes from 3 different TV series and demonstrate automatic annotation with an accuracy of 90% and up.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Continual Learning in Neural Networks (2019, methodology): “Most of the shots are outdoor and under varied lighting conditions, as also noted in [7].”