A LIGHTWEIGHT DEEP LEARNING MODEL FOR AGE AND GENDER IDENTITY PROBLEM USING THE CNN NETWORK

Abstract

Age and gender identification problems are gaining a lot of attention from researchers since social and multimedia networks are becoming more popular nowadays. Recently published methods have yielded quite good results in terms of accuracy but also proved ineffective in real-time identification because these models were designed too complicated. In this paper, we propose a lightweight model called lightweight CNN that performs parallel tasks of age and gender classification. In terms of accuracy in identifying age, lightweight CNN is 5.1% better than the best model recently published. About runtime and the number of parameters used, lightweight CNN uses much less than other models on the Adience dataset, meet the identification requirements in real time.