Face Retrieval in Videos using Face Quality Assessment and Convolution Neural Networks

EasyChair Preprint 4167

Abstract

With the large amount of videos produced every day, Content-Based Video Retrieval (CBVR) has become a necessity by describing each video with a compact and significant signature in order to efficiently retrieve the desired video from a large collection. In this work, we present a CBVR system applied on face recognition based on keyframes. The first step in this system consists to extract keyframes from video using Face Quality Assessment and Convolution Neural Networks. Starting by generating face quality scores for each face image using three face feature descriptors (Gabor,Local Binary Pattern (LBP) and Histogram of Oriented Gradient (HoG)). Then, we train a Convolution Neural Network (CNN) in a supervised manner in order to select frames having the best face quality. Experiments on several datasets has shown that the proposed “DeepFQA” method gives a promising results in terms of accuracy and precision/recall curve.

Keyphrases: Content Based Video Retrieval, Convolution Neural Network, Convolution Neural Network., Face Quality Assessment, keyframe extraction

@booklet{EasyChair:4167,
  author    = {Rahma Abed and Sahbi Bahroun and Ezzeddine Zagrouba},
  title     = {Face Retrieval in Videos using Face Quality Assessment and Convolution Neural Networks},
  howpublished = {EasyChair Preprint 4167},
  year      = {EasyChair, 2020}}