Empirical Study on Appearance-Based Binary Age Classification
Abstract
This chapter presents a systematic approach to designing a binary classifier using Support Vector Machines (SVMs). To exemplify the efficacy of the proposed approach, empirical studies were conducted in designing a classifier to classify people into different age groups using only appearance information from human facial images. Experiments were conducted to understand the effects of various issues that can potentially influence the performance of such a classifier. Linear data projection techniques such as Principal Component Analysis (PCA), Robust PCA (RPCA) and Non-Negative Matrix Factorization (NMF) were tested to find the best representation of the image data for designing the classifier. SVMs were used to learn the underlying model using the features extracted from the examples. Empirical studies were conducted to understand the influence of various factors such as preprocessing, image resolution, pose variation and gender on the classification of age group. The performances of the classifiers were also characterized in the presence of local feature occlusion and brightness gradients across the images. A number of experiments were conducted on a large data set to show the efficacy of the proposed approach. © 2005 John Wiley & Sons, Ltd.
Publication Title
Computer-Aided Intelligent Recognition Techniques and Applications
Recommended Citation
Yeasin, M., Khare, R., & Sharma, R. (2005). Empirical Study on Appearance-Based Binary Age Classification. Computer-Aided Intelligent Recognition Techniques and Applications, 241-255. https://doi.org/10.1002/0470094168.ch13
