SSR-NET: A compact soft stagewise regression network for age estimation

Tsun Yi Yang, Yi-Hsuan Huang, Yen Yu Lin, Pi Cheng Hsiu, Yung Yu Chuang

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

68 Scopus citations

Abstract

This paper presents a novel CNN model called Soft Stagewise Regression Network (SSR-Net) for age estimation from a single image with a compact model size. Inspired by DEX, we address age estimation by performing multi-class classification and then turning classification results into regression by calculating the expected values. SSR-Net takes a coarse-to-fine strategy and performs multi-class classification with multiple stages. Each stage is only responsible for refining the decision of its previous stage for more accurate age estimation. Thus, each stage performs a task with few classes and requires few neurons, greatly reducing the model size. For addressing the quantization issue introduced by grouping ages into classes, SSR-Net assigns a dynamic range to each age class by allowing it to be shifted and scaled according to the input face image. Both the multi-stage strategy and the dynamic range are incorporated into the formulation of soft stagewise regression. A novel network architecture is proposed for carrying out soft stagewise regression. The resultant SSR-Net model is very compact and takes only 0.32 MB. Despite its compact size, SSR-Net’s performance approaches those of the state-of-the-art methods whose model sizes are often more than 1500× larger.
Original languageAmerican English
Title of host publicationProceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18)
PublisherInternational Joint Conferences on Artificial Intelligence
Pages1078-1084
Number of pages7
ISBN (Print)9780999241127
DOIs
StatePublished - Jul 2018

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
Volume2018-July

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