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Access Type

WSU Access

Date of Award

January 2018

Degree Type

Thesis

Degree Name

M.S.

Department

Computer Science

First Advisor

Ming Dong

Abstract

Age is considered an important biometric trait for humans. Although there has been a growing interest in automatic age estimation based on a facial image, extracting robust aging features remains a challenging problem. Recent research shows that the aging patterns deeply learned from large-scale data lead to significant performance improvement. However, the insight about why and how deep learning models achieved superior performance is inadequate. In this paper, we propose to analyze, visualize and understand the deep aging patterns. We first train a series of convolutional neural network models for age estimation, and then illustrate the learning outcomes using feature maps, activation histograms, and deconvolution techniques. We also develop a visualization method that can compare the facial appearance and track its changes at different ages through the mapping between 2D images and a 3D face template. Our framework provides an innovative way to understand human facial aging process from a machine perspective.

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