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CLC number: TP391

On-line Access: 2015-11-04

Received: 2015-04-20

Revision Accepted: 2015-05-15

Crosschecked: 2015-10-16

Cited: 3

Clicked: 2188

Citations:  Bibtex RefMan EndNote GB/T7714


Ying Cai


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Frontiers of Information Technology & Electronic Engineering  2015 Vol.16 No.11 P.930-939


Multiclass classification based on a deep convolutional network for head pose estimation

Author(s):  Ying Cai, Meng-long Yang, Jun Li

Affiliation(s):  School of Computer Science, Sichuan University, Chengdu 610065, China; more

Corresponding email(s):   caiying34@qq.com, steinbeck@163.com, ljun402@163.com

Key Words:  Head pose estimation, Deep convolutional neural network, Multiclass classification

Ying Cai, Meng-long Yang, Jun Li. Multiclass classification based on a deep convolutional network for head pose estimation[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(11): 930-939.

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author="Ying Cai, Meng-long Yang, Jun Li",
journal="Frontiers of Information Technology & Electronic Engineering",
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A1 - Ying Cai
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A1 - Jun Li
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DOI - 10.1631/FITEE.1500125

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.

This paper uses convolutional neural networks for pose estimation. This method is evaluated on the CAS-PEAL-R1 database, the CMU PIE database and the CUBIC FACEPIX database. The idea is simple, but seems working for the experimental results.




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