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Conference Paper
Real-Time Age and Gender Estimation from Face Images
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- Authors
- Jang-Hee Yoo, So-Hee Park, Yongjin Lee
- Issue Date
- 2017-11
- Citation
- International Conference on Machine Learning and Data Engineering (iCMLDE) 2017, pp.15-20
- Language
- English
- Type
- Conference Paper
- Abstract
- In this paper, we describe an automated real-time system that estimates age and gender by utilizing a set of facial image sequences from a video camera. The age and gender estimation system consists of four steps: i) detection and extraction of the facial region from input video; ii) selection of the frontal face images from the extracted facial regions using head pose estimation; iii) duplicated face detection and removal by tracking the faces; and iv) age and gender estimation using statistical facial features. Here, LBP features with AdaBoost classifiers are used to detect the face region in a video frame, and the frontal face images are selected using a 3D pose estimation method. In addition, a particle filter-based tracking framework is employed to remove duplicated faces and to improve the accuracy of people counting, and Gabor-LBP features are used to estimate age and gender using a linear SVM and Adaboost classifiers. In experiments, a large number of face datasets are used to train and evaluate the proposed method, and higher performance is achieved in terms of age and gender estimation: 72.53% for age and 98.90% for gender.
- KSP Keywords
- 3D pose estimation, Detection and extraction, Detection and removal, Face image, Facial image, Filter-based, Gender estimation, Head pose estimation, Higher performance, Image sequences, LBP features