Paper Info
Title
Multiclass classification based on a deep convolutional network for head pose estimation
Abstract
Head pose estimation has been considered an important and challenging task in computer vision. In this paper we propose a novel method to estimate head pose based on a deep convolutional neural network (DCNN) for 2D face images. We design an effective and simple method to roughly crop the face from the input image, maintaining the individual-relative facial features ratio. The method can be used in various poses. Then two convolutional neural networks are set up to train the head pose classifier and then compared with each other. The simpler one has six layers. It performs well on seven yaw poses but is somewhat unsatisfactory when mixed in two pitch poses. The other has eight layers and more pixels in input layers. It has better performance on more poses and more training samples. Before training the network, two reasonable strategies including shift and zoom are executed to prepare training samples. Finally, feature extraction filters are optimized together with the weight of the classification component through training, to minimize the classification error. Our method has been evaluated on the CAS-PEAL-R1, CMU PIE, and CUBIC FacePix databases. It has better performance than state-of-the-art methods for head pose estimation.
Year
DOI
Venue
2015
10.1631/FITEE.1500125
Frontiers of IT & EE
Keywords
Field
DocType
Head pose estimation, Deep convolutional neural network, Multiclass classification, A, TP391
Computer vision,Pattern recognition,Convolutional neural network,Computer science,Zoom,Feature extraction,Pose,Pixel,Artificial intelligence,Classifier (linguistics),Multiclass classification
Journal
Volume
Issue
ISSN
16
11
2095-9230
Citations 
PageRank 
References 
6
0.44
19
Authors
3
Name
Order
Citations
PageRank
Ying Cai1101.85
Menglong Yang210910.49
Jun Li3398.57