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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Imane Hachchane
Laboratory Of Electronics, Energy, Automation & Information Processing, Faculty Of Sciences And Techniques Mohammedia, University Hassan II Casablanca, Mohammedia, Morocco
Computer Science & IT Engineering

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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) Vol 11, No 1: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i1.pp102-109

Abstract

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.
Large-scale image-to-video face retrieval with convolutional neural network features Imane Hachchane; Abdelmajid Badri; Aïcha Sahel; Yassine Ruichek
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (341.601 KB) | DOI: 10.11591/ijai.v9.i1.pp40-45

Abstract

Convolutional neural network features are becoming the norm in instance retrieval. This work investigates the relevance of using an of the shelf object detection network, like Faster R-CNN, as a feature extractor for an image-to-video face retrieval pipeline instead of using hand-crafted features. We use the objects proposals learned by a Region Proposal Network (RPN) and their associated representations taken from a CNN for the filtering and the re-ranking steps. Moreover, we study the relevance of features from a finetuned network. In addition to that we explore the use of face detection, fisher vector and bag of visual words with those CNN features. We also test the impact of different similarity metrics. The results obtained are very promising.
Co-Authors Abdelmajid Badri Abdelmajid Badri Aïcha Sahel Aïcha Sahel Ilham Elmourabit Yassine Ruichek Yassine Ruichek