Age and gender estimation using Region-SIFT and multi-layered SVM

Hyunduk Kim; Sang-Heon Lee; Myoung-Kyu Sohn; Byunghun Hwang

doi:10.1117/12.2309441

13 April 2018 Age and gender estimation using Region-SIFT and multi-layered SVM

Hyunduk Kim, Sang-Heon Lee, Myoung-Kyu Sohn, Byunghun Hwang

Proceedings Volume 10696, Tenth International Conference on Machine Vision (ICMV 2017); 106962J (2018) https://doi.org/10.1117/12.2309441
Event: Tenth International Conference on Machine Vision, 2017, Vienna, Austria

Abstract

In this paper, we propose an age and gender estimation framework using the region-SIFT feature and multi-layered SVM classifier. The suggested framework entails three processes. The first step is landmark based face alignment. The second step is the feature extraction step. In this step, we introduce the region-SIFT feature extraction method based on facial landmarks. First, we define sub-regions of the face. We then extract SIFT features from each sub-region. In order to reduce the dimensions of features we employ a Principal Component Analysis (PCA) and a Linear Discriminant Analysis (LDA). Finally, we classify age and gender using a multi-layered Support Vector Machines (SVM) for efficient classification. Rather than performing gender estimation and age estimation independently, the use of the multi-layered SVM can improve the classification rate by constructing a classifier that estimate the age according to gender. Moreover, we collect a dataset of face images, called by DGIST_C, from the internet. A performance evaluation of proposed method was performed with the FERET database, CACD database, and DGIST_C database. The experimental results demonstrate that the proposed approach classifies age and performs gender estimation very efficiently and accurately.

Citation Download Citation

Hyunduk Kim, Sang-Heon Lee, Myoung-Kyu Sohn, and Byunghun Hwang "Age and gender estimation using Region-SIFT and multi-layered SVM", Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106962J (13 April 2018); https://doi.org/10.1117/12.2309441

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