Using weighted ranking with classification for facial age estimation = 가중 랭킹과 분류 방법을 활용한 얼굴 나이 인식 방법

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We propose an end-to-end deep learning framework for age estimation using face images. Our key observation is that ranking face images by age plays an important role for learning features and estimating age. We thus exploit a ranking objective jointly with an age classification objective. In this joint configuration, the ranking objective provides relative information to a deep model, that produces higher accuracy. For the ranking objective, we use a triplet ranking strategy with two novel schemes: relative triplet selection and weighted triplet ranking loss. First, the relative triplet selection expands a pool of possible triplets, enabling effective learning for ranking. Second, the weighted triplet ranking loss reflects the relativeness of age and considers its varying importance for learning. We have applied our method to two famous age estimation benchmarks, Adience and FG-NET, and demonstrated that our approach achieves meaningful improvement over the state-of-the-art age estimation methods.
Advisors
Yang, Hyun Seungresearcher양현승researcher
Description
한국과학기술원 :전산학부,
Publisher
한국과학기술원
Issue Date
2018
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전산학부, 2018.2,[ii, 19 p. :]

Keywords

Facial age recognition▼adeep learning▼aranking loss▼aartificial neural network; 얼굴 나이 인식▼a딥 러닝▼a랭킹 손실 함수▼a인공 신경망

URI
http://hdl.handle.net/10203/267061
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=734093&flag=dissertation
Appears in Collection
CS-Theses_Master(석사논문)
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