Robust deep learning features for face recognition under mismatched conditions

Omid Abdollahi Aghdam, Hazim Kemal Ekenel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

In this paper, we addressed the problem of face recognition under mismatched conditions. In the proposed system, for face representation, we leveraged the state-of-the-art deep learning models trained on the VGGFace2 dataset. More specifically, we used pretrained convolutional neural network models to extract 2048 dimensional feature vectors from face images of International Challenge on Biometric Recognition in the Wild dataset, shortly, ICB-RW 2016. In this challenge, the gallery images were collected under controlled, indoor studio settings, whereas probe images were acquired from outdoor surveillance cameras. For classification, we trained a nearest neighbor classifier using correlation as the distance metric. Experiments on the ICB-RW 2016 dataset have shown that the employed deep learning models that were trained on the VGGFace2 dataset provides superior performance. Even using a single model, compared to the ICB-RW 2016 winner system, around 15% absolute increase in Rank-1 correct classification rate has been achieved. Combining individual models at feature level has improved the performance further. The ensemble of four models achieved 91.8% Rank-1, 98.0% Rank-5 identification rate, and 0.997 Area Under the Curve of Cumulative Match Score on the probe set. The proposed method significantly outperforms the Rank-1, Rank-5 identification rates, and Area Under the Curve of Cumulative Match Score of the best approach at the ICB-RW 2016 challenge, which were 69.8%, 85.3%, and 0.954, respectively.

Original languageEnglish
Title of host publication26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-4
Number of pages4
ISBN (Electronic)9781538615010
DOIs
Publication statusPublished - 5 Jul 2018
Event26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 - Izmir, Turkey
Duration: 2 May 20185 May 2018

Publication series

Name26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

Conference

Conference26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Country/TerritoryTurkey
CityIzmir
Period2/05/185/05/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

  • Biometric Recognition
  • Convolutional Neural Networks
  • Deep Learning
  • Face Recognition

Fingerprint

Dive into the research topics of 'Robust deep learning features for face recognition under mismatched conditions'. Together they form a unique fingerprint.

Cite this