Image and video face retrieval with query image using convolutional neural network features - Imane Hachchane; Abdelmajid Badri; Aïcha Sahel; Ilham Elmourabit; Yassine Ruichek (IAES International Journal of Artificial Intelligence (IJ-AI), 2022)
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.