Open AccessAutomatic face recognition for home safety using video‐based side‐view face images
Author(s) -
Santemiz Pinar,
Spreeuwers Luuk J.,
Veldhuis Raymond N.J.
Publication year - 2018
Publication title -
iet biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 28
eISSN - 2047-4946
pISSN - 2047-4938
DOI - 10.1049/iet-bmt.2017.0203
Subject(s) - computer science , facial recognition system , face (sociological concept) , artificial intelligence , task (project management) , computer vision , doors , face detection , three dimensional face recognition , pattern recognition (psychology) , social science , management , sociology , economics , operating system
Face recognition from side‐view positions is an essential task for recognition systems with real‐world scenarios. Most of the existing face recognition methods rely on alignment of face images into some canonical form. However, alignment in side‐view faces can be challenging due to lack of symmetry and a small number of reliable reference points. To the best of the author's knowledge, only a few of the existing methods deal with video‐based face recognition from side‐view images, and not many databases include sufficient video footage to study this task. Here, the authors propose an automatic side‐view face recognition system designed for home safety applications. They first contribute a newly collected video face database, named UT‐DOOR, where 98 subjects were recorded with four cameras attached at doorposts as they pass through doors. Secondly, they propose a face recognition system, where they automatically detect and recognise faces using side‐view images in videos. One of the attractive properties of this system is that they use cameras with limited view angle to preserve the privacy of the people. They review several databases and test their system both on the CMU Multi‐PIE database and the UT‐DOOR database for comparison. Experimental results show that their system can successfully recognise side‐view faces from videos.