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Image-based gender classification and age estimation of human are classic problems in computer vision. Most of researches in this field focus just only one task of either gender classification or age estimation and most of the reported methods for each task focus on accuracy performance and are not computationally light. Thus, running both tasks together simultaneously on low cost mobile or embedded systems with limited cpu processing speed and memory capacity are practically prohibited. In this paper, we propose a novel light-weight gender classification and age estimation method based on ensemble multitasking deep learning with light-weight processing neural network architecture, which processes both gender classification and age estimation simultaneously and in real-time even for embedded systems. Through experiments over various well-known datasets, it is shown that the proposed method performs comparably to the state-of-the-art gender classification and/or age estimation methods with respect to accuracy and runs fast enough (average 14fps) on a Jestson Nano embedded board.

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기사명 저자명 페이지 원문 목차
영상 생성적 데이터 증강을 이용한 딥러닝 기반 SAR 영상 선박 탐지 = Deep-learning based SAR ship detection with generative data augmentation 권형준, 정소미, 김성태, 이재석, 손광훈 p. 1-9
발열 감지, 안면 마스크 착용 검출, 전자출입명부 QR 코드 체킹을 지원하는 보급형 COVID-19 디지털 사이니지 플레이어 설계 및 구현 = Design and implementation of entry-level COVID-19 digital signage player supporting fever detection, face mask wearing detection and KI-pass QR code checking 쩐꾸억바오후이, 박상군, 정선태 p. 10-28
잡음제거 합성곱 신경망을 이용한 이미지 복원 방법 = Image restoration method using denoising CNN 김선재, 이정호, 이석환, 전동산 p. 29-38
앙상블 멀티태스킹 딥러닝 기반 경량 성별 분류 및 나이별 추정 = Light-weight gender classification and age estimation based on ensemble multi-tasking deep learning 쩐꾸억바오후이, 박종현, 정선태 p. 39-51
Intra-class local descriptor-based prototypical network for few-shot learning Xi-Lang Huang, Seon Han Choi p. 52-60
유사성 모델 기반의 수중 다중매체 통신 라우팅 프로토콜스택 선택방법 = A method for the selection of underwater multimedia routing protocol stack based on the similarity model 신동현, 김창화 p. 61-71
객체 추적을 위한 보틀넥 기반 Siam-CNN 알고리즘 = Bottleneck-based Siam-CNN algorithm for object tracking 임수창, 김종찬 p. 72-81
SARIMA 모델을 이용한 태양광 발전량 예측 연구 = A research of prediction of photovoltaic power using SARIMA model 정하영, 홍석훈, 전재성, 임수창, 김종찬, 박형욱, 박철영 p. 82-91
마약류 범죄의 사례 연구 및 문화콘텐츠를 활용한 예방과 인식 개선 방안 = Cases for narcotic crimes and solutions to prevent and improve the awareness with cultural contents 이연우 p. 92-102
소셜미디어 사용자가 만드는 동영상 광고효과에 관한 연구 : A study on the effectiveness of video advertisements generated by social media users : centered on video content type and information framework / 동영상 콘텐츠 유형 및 정보 프레임워크 중심으로 장녕, 김치용 p. 103-113
하이퍼리얼리즘형 웹드라마 <좋좋소>의 콘텐츠 특성 연구 = A study on content characteristics of hyperrealism web drama <Jot-Jot-So> 이준석, 정원식 p. 114-123
VR 콘텐츠가 재한 중국인 유학생 아증후군적 우울 상태에 미치는 영향 연구 : A study on the effect of VR content on sub-syndromatic depression of Chinese students in Korea : based on attention restoration theory (ART) / 주의력회복이론을 기반으로 정선요, 이연우, 김치용 p. 124-134
민화 DB를 위한 분류체계 설계 = Designing a classification system for Minhwa DB 최은진, 이영숙 p. 135-143

참고문헌 (33건) : 자료제공( 네이버학술정보 )

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번호 참고문헌 국회도서관 소장유무
1 R.A. Jules, R. Tapamo, and A.O. Adewumi, “Age Estimation via Face Images: a Survey,”EURASIP Journal on Image and Video Processing, Article No. 42, pp. 1-35, 2018. 미소장
2 P. Punyani, R. Gupta, and A. Kumar, “Neural Networks for Facial Age Estimation: a Survey on Recent Advances,” Artificial Intelligence Review, Vol. 53, pp. 3299–3347, 2020. 미소장
3 K. Oh, and S.-K. Kwon, “Implementation of Character Floating Hologram by Age and Gender Recognitions using Depth Images,”Journal of Korea Multimedia Society, Vol. 22, No. 2, pp. 146-156, 2019. 미소장
4 S. Ruder, “An Overview of Multi-Task Learning in Deep Neural Networks,” arXiv Preprint, arXiv:1706.05098, 2017. 미소장
5 A.G. Howard, et al., “Mobilenets: Efficient Convolutional Neural Networks For Mobile Vision Applications,” arXiv Preprint, arXiv:1704.04861, 2017. 미소장
6 R.O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification, 2nd Edition, Wiley. 미소장
7 R. Caruana, “Multitask Learning,” Machine Learning, Vol. 28, pp. 41-75, 1997. 미소장
8 R. Rothe, R. Timofte, and L. Van Gool, “Dex:Deep Expectation of Apparent Age from a Single Image,” Proc. IEEE Int. Conf. on Computer Vision Workshops, pp. 252-257, 2015. 미소장
9 H. Pan, H. Han, S. Shan, and X. Chen, “Mean-Variance Loss for Deep Age Estimation from a Face,” IEEE Conf. on Computer Vision and Pattern Recognition, pp. 5285-5294, 2018. 미소장
10 K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv Preprint, arXiv: 1409.1556, 2015. 미소장
11 E. Agustsson, R. Timofte, and L. Van Gool, “Anchored Regression Networks Applied to Age Estimation and Super Resolution,” Int. Conf. on Computer Vision, pp. 1652-1661, 2017 미소장
12 G. Levi and T. Hassner. “Age and Gender Classification Using Convolutional Neural NetWorks,” IEEE Conf. on Computer Vision and Pattern Recognition, pp. 34-42, 2015. 미소장
13 M. Duan, et al. “A Hybrid Deep Learning CNN –ELM for Age and Gender Classification,”Neurocomputing, Vol. 275, pp. 448-461, 2018. 미소장
14 S. Chen, C. Zhang, M. Dong, J. Le, and M. Rao, “Using Ranking-CNN for age estimation,”IEEE Conf. on Computer Vision and Pattern Recognition, pp. 5183-5192, 2017. 미소장
15 B.-B. Gao, H.-Y. Zhou, J. Wu, and X. Geng, ‘‘Age Estimation Using Expectation of Label Distribution Learning,” Proceedings of the 27th. International Joint Conference on Artificial Intelligence, pp. 712-718, 2018. 미소장
16 O. Agbo-Ajala and S. Viriri, “A Lightweight Convolutional Neural Network for Real and Apparent Age Estimation in Unconstrained Face Images,” IEEE Access, Vol. 8, pp. 162800-162808, 2020. 미소장
17 H. Sun, H. Pan, H. Han, and S. Shan, “Deep Conditional Distribution Learning for Age Estimation,” IEEE Transactions on Information Forensics and Security, Vol. 16, pp. 4679-4690, 2021. 미소장
18 G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” IEEE Conf. on Computer Vision and Pattern Recognition, pp. 2261-2269, 2017. 미소장
19 N. Ma, X. Zhang, H.-T. Zheng, and J. Sun, “Shuffleenet v2: Practical Guidelines for Efficient CNN Architecture Design,“ Proceedings of the European Conference on Computer Vision (ECCV) , pp. 116-131, 2018. 미소장
20 T.-Y. Yang, et al., “SSR-Net: A Compact Soft Stagewise Regression Network for Age Estimation,”Int. Joint Conf. on Artificial Intelligence Organization, pp. 1078-1084, 2018. 미소장
21 X. Liu, Y. Zou, H. Kuang, and X. Ma, “Face Image Age Estimation Based on Data Augmentation and Lightweight Convolutional Neural Network,” Symmetry, Vol. 146, Issue 1, 146, 2020. 미소장
22 B.I. Yoo, et al., “Deep Facial Age Estimation Using Conditional Multi-Task Learning with Weak Label Expansion,” IEEE Signal Processing Letters, Vol. 25, No. 6, pp. 808-812, 2018. 미소장
23 Y. Yang, et al., “Video System for Human Attribute Analysis Using Compact Convolutional Neural Network,” IEEE International Conference on Image Processing (ICIP), pp. 584-588. 2016. 미소장
24 J.-H. Lee, et al., “Joint Estimation of Age and Gender from Unconstrained Face Images Using Lightweight Multi-Task CNN for Mobile AppliCations,” IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), pp. 162-165, 2018. 미소장
25 N. Liu, F. Zhang, and F. Duan, “Facial Age Estimation Using a Multi-Task Network Combining Classification and Regression,” IEEE Access, Vol. 8, pp. 92441-92451, 2020. 미소장
26 N. Yu, et al., “Ensemble Learning for Facial Age Estimation within Non-Ideal Facial Imagery,”IEEE Access, Vol. 7, pp. 97938-97948, 2019. 미소장
27 Y. Zhou, H. Huttunen, and T. Elomaa, “Using Multiple Losses for Accurate Facial Age EstimaTion,” arXiv Preprint, arXiv:2106.09393, 2021. 미소장
28 K. Ricanek and T. Tesafaye, “Morph: A longitudinal image database of normal adult ageprogression,”7th Int. Conf. on Automatic Face and Gesture Recognition, 2006. 미소장
29 The FG-NET Aging Database, https://yan weifu.github.io/FG_NET_data/ (accessed November 30, 2021). 미소장
30 MegaAge-Asian Dataset, http://mmlab.ie.cuhk. edu.hk/projects/MegaAge/ (accessed November 30, 2021). 미소장
31 J. Cheng, Y. Li, J. Wang, L. Yu, and S. Wang, “Exploiting Effective Facial Patches for Robust Gender Recognition,” Tsinghua Science and Technology, Vol. 24, pp. 333-345, 2019. 미소장
32 IMDB-WIKI, https://data.vision.ee.ethz.ch/cvl/rrothe/IMDB-WIKI/ (accessed November 30, 2021). 미소장
33 Jetson Nano Board, https://elinux.org/Jetson_Nano (accessed November 30, 2021). 미소장

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