| Authors: |
Xiaoli Zhou
and Bir Bhanu
|
| Tags: |
algorithms,
genetic
programming
|
| Abstract: |
This paper introduces a new video based recognition
method to recognise non-cooperating individuals at a
distance in video, who expose side views to the camera.
Information from two biometric sources, side face and
gait, is used and integrated for recognition. For side
face, we construct Enhanced Side Face Image (ESFI), a
higher resolution image compared with the image
directly obtained from a single video frame, to fuse
information of face from multiple video frames. For
gait, we use Gait Energy Image (GEI), a spatio-temporal
compact representation of gait in video, to
characterise human walking properties. The features of
face and the features of gait are obtained separately
using Principal Component Analysis (PCA) and Multiple
Discriminant Analysis (MDA) combined method from ESFI
and GEI, respectively. They are then integrated at
match score level. Our approach is tested on a database
of video sequences corresponding to 46 people. The
different fusion methods are compared and analysed. The
experimental results show that (a) Integrated
information from side face and gait is effective for
human recognition in video; (b) The idea of
constructing ESFI from multiple frames is promising for
human recognition in video and better face features are
extracted from ESFI compared to those from original
face images. |
@inproceedings{bb52076,
title = {Integrating Face and Gait for Human Recognition},
author = {Xiaoli Zhou and Bir Bhanu},
booktitle = {Computer Vision and Pattern Recognition Workshop},
month = {17-22 June},
pages = {55},
publisher = {IEEE},
year = {2006},
abstract = {This paper introduces a new video based recognition
method to recognise non-cooperating individuals at a
distance in video, who expose side views to the camera.
Information from two biometric sources, side face and
gait, is used and integrated for recognition. For side
face, we construct Enhanced Side Face Image (ESFI), a
higher resolution image compared with the image
directly obtained from a single video frame, to fuse
information of face from multiple video frames. For
gait, we use Gait Energy Image (GEI), a spatio-temporal
compact representation of gait in video, to
characterise human walking properties. The features of
face and the features of gait are obtained separately
using Principal Component Analysis (PCA) and Multiple
Discriminant Analysis (MDA) combined method from ESFI
and GEI, respectively. They are then integrated at
match score level. Our approach is tested on a database
of video sequences corresponding to 46 people. The
different fusion methods are compared and analysed. The
experimental results show that (a) Integrated
information from side face and gait is effective for
human recognition in video; (b) The idea of
constructing ESFI from multiple frames is promising for
human recognition in video and better face features are
extracted from ESFI compared to those from original
face images.},
bibsource = {http://iris.usc.edu/Vision-Notes/bibliography/motion-f738.html#TT49185}, notes = {on GP??}, doi = {doi:10.1109/CVPRW.2006.103},
keywords = {algorithms, genetic programming }
}
::