TY - JOUR
T1 - An RGB-D based image set classification for robust face recognition from Kinect data
AU - Hayat, Munawar
AU - Bennamoun, Mohammed
AU - El-Sallam, Amar
N1 - Funding Information:
This work is supported by Student International Research Fees (SIRF) scholarship from the University of Western Australia (UWA), ARC Grants DPI10102166 and DP150100294 .
Publisher Copyright:
© 2015 Elsevier B.V.
PY - 2016
Y1 - 2016
N2 - The paper proposes a method for robust face recognition from low quality Kinect acquired images which have a wide range of variations in head pose, illumination, facial expressions, sunglass disguise and occlusions by hand. Multiple Kinect images of a person are considered as an image set and face recognition from these images is formulated as an RGB–D image set classification problem. The Kinect acquired raw depth data is used for pose estimation and an automatic cropping of the face region. Based upon the estimated poses, the face images of a set are divided into multiple image subsets. An efficient block based covariance matrix representation is proposed to model images in an image subset on Riemannian manifold (Lie group). For classification, SVM models are separately learnt for each image subset on the Lie group of Riemannian manifold and a fusion strategy is introduced to combine results from all image subsets. The proposed technique has been evaluated on a combination of three large data sets containing over 35,000 RGB–D images under challenging conditions. The proposed RGB–D based image set classification incurs low computational cost and achieves an identification rate as high as 99.5%
AB - The paper proposes a method for robust face recognition from low quality Kinect acquired images which have a wide range of variations in head pose, illumination, facial expressions, sunglass disguise and occlusions by hand. Multiple Kinect images of a person are considered as an image set and face recognition from these images is formulated as an RGB–D image set classification problem. The Kinect acquired raw depth data is used for pose estimation and an automatic cropping of the face region. Based upon the estimated poses, the face images of a set are divided into multiple image subsets. An efficient block based covariance matrix representation is proposed to model images in an image subset on Riemannian manifold (Lie group). For classification, SVM models are separately learnt for each image subset on the Lie group of Riemannian manifold and a fusion strategy is introduced to combine results from all image subsets. The proposed technique has been evaluated on a combination of three large data sets containing over 35,000 RGB–D images under challenging conditions. The proposed RGB–D based image set classification incurs low computational cost and achieves an identification rate as high as 99.5%
KW - face-recognition
KW - computer-vision
KW - image-processing
KW - Image set classification
KW - RGB-D Kinect data
KW - Face recognition
UR - http://www.scopus.com/inward/record.url?scp=84947022146&partnerID=8YFLogxK
UR - http://www.mendeley.com/research/rgbd-based-image-set-classification-robust-face-recognition-kinect-data
U2 - 10.1016/j.neucom.2015.07.027
DO - 10.1016/j.neucom.2015.07.027
M3 - Article
SN - 0925-2312
VL - 171
SP - 889
EP - 900
JO - Neurocomputing: an international journal
JF - Neurocomputing: an international journal
ER -