Earticle

초록

영어

This study proposes a method for estimating gender and age that is robust to various external environment changes by applying deep learning-based learning. To improve the accuracy of the proposed algorithm, an improved CNN network structure and learning method are described, and the performance of the algorithm is also evaluated. In this study, in order to improve the learning method based on CNN composed of 6 layers of hidden layers, a network using GoogLeNet's inception module was constructed. As a result of the experiment, the age estimation accuracy of 5,328 images for the performance test of the age estimation method is about 85%, and the gender estimation accuracy is about 98%. It is expected that real-time age recognition will be possible beyond feature extraction of face images if studies on the construction of a larger data set, pre-processing methods, and various network structures and activation functions have been made to classify the age classes that are further subdivided according to age.

목차

Abstract
1. INTRODUCTION
2. RELATED WORKS
3. ESTIMATION OF GENDER AND AGE OF FACE IMAGES USING CNN LEARNING
3.1 CNN-based gender and age estimation method
3.2 Design of CNN-based learning model (inception v1)
3.3 Dataset for learning
4. EXPERIMENT RESULTS
4.1 Face Detection
4.2 Gender Estimation
4.3 Age Estimation
5. CONCLUSION
ACKNOWLEDGEMENT
REFERENCES

키워드

저자

• Sooyeon Lim [ School of Game, Dongyang University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380