Open AccessImage and video face retrieval with query image using convolutional neural network features
Author(s) -
Imane Hachchane,
Abdelmajid Badri,
Aïcha Sahel,
Ilham Elmourabit,
Yassine Ruichek
Publication year - 2022
Publication title -
iaes international journal of artificial intelligence
Language(s) - English
Resource type - Journals
eISSN - 2252-8938
pISSN - 2089-4872
DOI - 10.11591/ijai.v11.i1.pp102-109
Subject(s) - computer science , convolutional neural network , artificial intelligence , pooling , image retrieval , focus (optics) , feature (linguistics) , exploit , pipeline (software) , pattern recognition (psychology) , ranking (information retrieval) , face (sociological concept) , image (mathematics) , computer vision , social science , linguistics , philosophy , physics , computer security , sociology , optics , programming language
This paper addresses the issue of image and video face retrieval. The aim of this work is to be able to retrieve images and/or videos of specific person from a dataset of images and videos if we have a query image of that person. The methods proposed so far either focus on images or videos and use hand crafted features. In this work we built an end-to-end pipeline for both image and video face retrieval where we use convolutional neural network (CNN) features from an off-line feature extractor. And we exploit the object proposals learned by a region proposal network (RPN) in the online filtering and re-ranking steps. Moreover, we study the impact of finetuning the networks, the impact of sum-pooling and max-pooling, and the impact of different similarity metrics. The results that we were able to achieve are very promising.