Understanding videos with face recognition: a complete pipeline and applications

Pasquale Lisena*, Jorma Laaksonen, Raphaël Troncy

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review


When browsing or studying a video corpus, particularly relevant information consists in knowing who are the people appearing in the scenes. In this paper, we show how a combination of state of the art techniques can be organised in a pipeline for face recognition of celebrities. In particular, we propose a system which combines MTCNN for detecting faces and FaceNet for extracting face embeddings, which are used to train a set of classifiers. The face recognition results obtained at a frame level are then combined with those in consecutive frames, relying on automatic object tracking. Differently from previous work, we use images automatically retrieved by web search engines. We evaluate the systems one three datasets including historical videos from 1945 to 1969 and contemporary videos, obtaining a good precision score. In addition, we show how the obtained results can be applied to foster historical studies.

Original languageEnglish
Pages (from-to)2147-2159
Number of pages13
Issue number6
Early online date15 Jun 2022
Publication statusPublished - Dec 2022
MoE publication typeA1 Journal article-refereed


  • Computer vision
  • Face recognition
  • Knowledge graphs
  • Web image retrieval


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