Age and gender estimation using deep residual learning network - Lee, Seok Hee; Hosseini, Sepidehsadat; Kwon, Hyuk Jin; Moon, Jaewon; Koo, Hyung Il; Cho, Nam Ik (2018 International Workshop on Advanced Image Technology, IWAIT 2018, 2018)
In this paper, we propose a deep residual learning model for age and gender estimation. Our method detects faces in input images, and then the age and gender of each face are estimated. The estimation method consists of three deep neural networks where we adopt residual learning methods. We train the model with IMDB-WIKI database [4]. However, since the database has only a small number of face images under the age of 20, we augment the set by collecting the images on the Internet. Experimental results show that the proposed model with residual learning yields improved performance.
Keywords: Age estimation; Estimation methods; Gender estimations; Input image; Learning methods; Learning models; Learning network; Residual learning; Age estimation; Deep learning; Gender estimation; Image processing; Machine learning; Residual learning