Using Dimensionality Reduction to Improve Local Regression on Facial Age Estimation
Date Issued
2015
Date
2015
Author(s)
Jian, Jia-Hong
Abstract
In this thesis, we focus on using dimensionality reduction algorithms to let k-nearest neighbor have better results so that we could get better performance on local regression which we used on human face age determination. Our framework was based on Chao’s method and we do some optimization. The common feature extraction technique is applied in this field, active appearance model (AAM), which can jointly represent the shape and texture variations of human face. In order to discover both geometrical and discriminant structures of the data manifold, maximum margin projection (MMP) is used for dimensionality reduction and causes the margin between relevant and irrelevant classes is maximized. For enhancing the influence of some specific dimensionality and overcome the disadvantage of RCA from Chao’s method, we leverage discriminant component analysis (DCA) instead, which is according to covariance analysis to do distance metric adjustment. Because of the advantage of neighbor preserving as mentioned, we use local regression instead of global regression for age determination. Furthermore, the proposed method can lighten the problem of dataset imbalance and ordinal relationship. Finally, we get 5.6242 MAE which is improved by 0.2 errors, comparing to Chao’s method.
Subjects
active appearance model
manifold learning
distance metric learning
local regression
mean absolute error
Type
thesis
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