Facial Point Classifier using Convolution Neural Network and Cascade Facial Point Detector

Je-Hun Yu; Kwang-Eun Ko; Kwee-Bo Sim

Facial Point Classifier using Convolution Neural Network and Cascade Facial Point Detector

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컨볼루셔널 신경망과 케스케이드 안면 특징점 검출기를 이용한 얼굴의 특징점 분류

Type: Academic journal

Author: Je-Hun Yu (중앙대학교) Kwang-Eun Ko Kwee-Bo Sim (중앙대학교)

Journal: Institute of Control, Robotics and Systems Journal of Institute of Control, Robotics and Systems Vol.22 No.3 KCI Accredited Journals SCOPUS

Published: 2016.3

Pages: 241 - 246 (6page)

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Facial Point Classifier using Convolution Neural Network and Cascade Facial Point Detector

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Nowadays many people have an interest in facial expression and the behavior of people. These are human-robot interaction (HRI) researchers utilize digital image processing, pattern recognition and machine learning for their studies. Facial feature point detector algorithms are very important for face recognition, gaze tracking, expression, and emotion recognition. In this paper, a cascade facial feature point detector is used for finding facial feature points such as the eyes, nose and mouth. However, the detector has difficulty extracting the feature points from several images, because images have different conditions such as size, color, brightness, etc. Therefore, in this paper, we propose an algorithm using a modified cascade facial feature point detector using a convolutional neural network. The structure of the convolution neural network is based on LeNet-5 of Yann LeCun. For input data of the convolutional neural network, outputs from a cascade facial feature point detector that have color and gray images were used. The images were resized to 32×32 . In addition, the gray images were made into the YUV format. The gray and color images are the basis for the convolution neural network. Then, we classified about 1,200 testing images that show subjects. This research found that the proposed method is more accurate than a cascade facial feature point detector, because the algorithm provides modified results from the cascade facial feature point detector.

#cascade facial point detector #convolutional neural network #YUV format #human-robot interaction

Abstract
Ⅰ. 서론
Ⅱ. 관련 연구
Ⅲ. 제안기술
Ⅳ. 실험 결과 및 분석
Ⅴ. 결론
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J. H. Yu, S. M. Park, K. E. Ko, and K. B. Sim, “Face classification using cascade facial detection and convolutional neural network,” Proc. of Korean Institute of Intelligent Systems Fall Conference, vol. 25, no. 2, pp. 157-159, 2015.

R. Cowie, E. Douglas-Cowie, N. Tsapatsoulis, G. Votsis, S. Kollias, W. Fellenz, and J. G. Taylor, “Emotion recognition in human-computer interaction,” IEEE Signal Processing Magazine, vol. 18, no. 1, pp. 32-80, Jan. 2001.

M. Valstar, B. Martinez, X. Binefa, and M. Pantic, “Facial point detection using boosted regression and graph models,” Proc. of 2010 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2729-2736, 2010.

M. F. Valstar, I. Patras, and M. Pantic, “Facial action unit detection using probabilistic actively learned support vector machines on tracked facial point data,” Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 76-83, Jun. 2005.

Y. Sun, X. Wang, and X. Tang, “Deep convolutional network cascade for facial point detection,” Proc. of 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3476-3483, Jun. 2013.

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Je-Hun Yu

Affiliation Chung-Ang University

Fields Engineering > Electrical and Electronic Engineering > Information and Communication Engineering Engineering > Mechanical Engineering > General Mechanical Engineering

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Affiliation 한국생산기술연구원

Fields Engineering > Electrical and Electronic Engineering > Information and Communication Engineering TOP 5% Engineering > Electrical and Electronic Engineering > Control and Instrumentation Engineering TOP 0.5%

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Affiliation 중앙대학교

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UCI(KEPA) : I410-ECN-0101-2016-569-002648667

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