Details

Autor(en) / Beteiligte

• Ciaparrone, Gioele
• Chiariglione, Leonardo
• Tagliaferri, Roberto

Titel

A comparison of deep learning models for end-to-end face-based video retrieval in unconstrained videos

Ist Teil von

• Neural computing & applications, 2022-05, Vol.34 (10), p.7489-7506

Ort / Verlag

London: Springer London

Erscheinungsjahr

2022

Link zum Volltext

Quelle

SpringerLink

Beschreibungen/Notizen

• Face-based video retrieval (FBVR) is the task of retrieving videos that containing the same face shown in the query image. In this article, we present the first end-to-end FBVR pipeline that is able to operate on large datasets of unconstrained, multi-shot, multi-person videos. We adapt an existing audiovisual recognition dataset to the task of FBVR and use it to evaluate our proposed pipeline. We compare a number of deep learning models for shot detection, face detection, and face feature extraction as part of our pipeline on a validation dataset made of more than 4000 videos. We obtain 97.25% mean average precision on an independent test set, composed of more than 1000 videos. The pipeline is able to extract features from videos at ∼ 7 times the real-time speed, and it is able to perform a query on thousands of videos in less than 0.5 s.