TY - GEN
T1 - Face recognition using scattering convolutional network
AU - Minaee, Shervin
AU - Abdolrashidi, Amirali
AU - Wang, Yao
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - Face recognition has been an active research area in the past few decades. In general, face recognition can be very challenging due to variations in viewpoint, illumination, facial expression, etc. Therefore it is essential to extract features which are invariant to some or all of these variations. Here a new image representation, called scattering trans-form/network, has been used to extract features from faces. The scattering transform is a kind of convolutional network which provides a powerful multi-layer representation for signals. After extraction of scattering features, PCA is applied to reduce the dimensionality of the data and then a multi-class support vector machine is used to perform recognition. The proposed algorithm has been tested on three face datasets and achieved a very high recognition rate.
AB - Face recognition has been an active research area in the past few decades. In general, face recognition can be very challenging due to variations in viewpoint, illumination, facial expression, etc. Therefore it is essential to extract features which are invariant to some or all of these variations. Here a new image representation, called scattering trans-form/network, has been used to extract features from faces. The scattering transform is a kind of convolutional network which provides a powerful multi-layer representation for signals. After extraction of scattering features, PCA is applied to reduce the dimensionality of the data and then a multi-class support vector machine is used to perform recognition. The proposed algorithm has been tested on three face datasets and achieved a very high recognition rate.
UR - http://www.scopus.com/inward/record.url?scp=85050656188&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050656188&partnerID=8YFLogxK
U2 - 10.1109/SPMB.2017.8257025
DO - 10.1109/SPMB.2017.8257025
M3 - Conference contribution
AN - SCOPUS:85050656188
T3 - 2017 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2017 - Proceedings
SP - 1
EP - 6
BT - 2017 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2017
Y2 - 2 December 2017
ER -