[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.14801/jkiit.2018.16.11.105

A Study of Facial Organs Classification System Based on Fusion of CNN Features and Haar-CNN Features

Hao, Biao (Dong-A University Electronic Engineering)
Lim, Hye-Youn (Dong-A University Electronic Engineering)
Kang, Dae-Seong (Dong-A University Electronic Engineering)

Publication Information

The Journal of Korean Institute of Information Technology / v.16, no.11, 2018 , pp. 105-113 More about this Journal

Abstract

In this paper, we proposed a method for effective classification of eye, nose, and mouth of human face. Most recent image classification uses Convolutional Neural Network(CNN). However, the features extracted by CNN are not sufficient and the classification effect is not too high. We proposed a new algorithm to improve the classification effect. The proposed method can be roughly divided into three parts. First, the Haar feature extraction algorithm is used to construct the eye, nose, and mouth dataset of face. The second, the model extracts CNN features of image using AlexNet. Finally, Haar-CNN features are extracted by performing convolution after Haar feature extraction. After that, CNN features and Haar-CNN features are fused and classify images using softmax. Recognition rate using mixed features could be increased about 4% than CNN feature. Experiments have demonstrated the performance of the proposed algorithm.

Keywords

AlexNet; CNN; softmax classifier; Haar-CNN; image classification;

Citations & Related Records

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