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

Title & Authors
Facial Point Classifier using Convolution Neural Network and Cascade Facial Point Detector
Yu, Je-Hun; Ko, Kwang-Eun; Sim, Kwee-Bo;

Abstract
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 $\small{32{\times}32}$. 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.
Keywords
cascade facial point detector;convolutional neural network;YUV format;human-robot interaction;
Language
Korean
Cited by
1.
Mobile Robot Control using Smart Phone for internet of Things, Journal of Korean Institute of Intelligent Systems, 2016, 26, 5, 396
2.
Robust lip detection based on histogram of oriented gradient features and convolutional neural network under effects of light and background, Optik - International Journal for Light and Electron Optics, 2017, 136, 462
3.
Dictionary learning feature space via sparse representation classification for facial expression recognition, Artificial Intelligence Review, 2017