Investigating Nuisance Factors in Face Recognition with DCNN Representation
- 1 July 2017
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE) in 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- p. 583-591
- https://doi.org/10.1109/cvprw.2017.86
Abstract
Deep learning based approaches proved to be dramatically effective to address many computer vision applications, including "face recognition in the wild". It has been extensively demonstrated that methods exploiting Deep Convolutional Neural Networks (DCNN) are powerful enough to overcome to a great extent many problems that negatively affected computer vision algorithms based on hand-crafted features. These problems include variations in illumination, pose, expression and occlusion, to mention some. The DCNNs excellent discriminative power comes from the fact that they learn low-and high-level representations directly from the raw image data. Considering this, it can be assumed that the performance of a DCNN are influenced by the characteristics of the raw image data that are fed to the network. In this work, we evaluate the effect of different bounding box dimensions, alignment, positioning and data source on face recognition using DCNNs, and present a thorough evaluation on two well known, public DCNN architectures.Keywords
This publication has 16 references indexed in Scilit:
- Effective 3D based frontalization for unconstrained face recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- Triplet probabilistic embedding for face verification and clusteringPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- Pose-Aware Face Recognition in the WildPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- An End-to-End System for Unconstrained Face Verification with Deep Convolutional Neural NetworksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Dictionary Learning Based 3D Morphable Model Construction for Face Recognition with Varying Expression and PosePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark APublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- One millisecond face alignment with an ensemble of regression treesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014
- Using 3D Models to Recognize 2D Faces in the WildPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2013
- Face recognition using Histograms of Oriented GradientsPattern Recognition Letters, 2011
- SIFT features for face recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2009