Abstract
In this paper, a novel discriminant analysis named two-dimensional Heteroscedastic Discriminant Analysis (2DHDA) is presented, and used for gender classification. In 2DHDA, equal within-class covariance constraint is removed. Firstly, the criterion of 2DHDA is defined according to that of 2DLDA. Secondly, the criterion of 2DHDA, log and rearranging terms are taken, and then the optimal projection matrix is solved by gradient descent algorithm. Thirdly, face images are projected onto the optimal projection matrix, thus the 2DHDA features are extracted. Finally, Nearest Neighbor classifier is selected to perform gender classification. Experimental results show that higher recognition rate is obtained by way of 2DHDA compared with 2DLDA and HDA .
Highlights
Nearest Neighbor classifier is selected to perform gender classification
Face images are projected onto the whole, θ2DHDA the most discriminant features are could not extracted, former d column vectors of θ2DHDA are selected as projection axes, the extracted features expressed as where θ2DHDA (:,1: d ) denotes the former d column vectors of matrix of sample A
When the gradient descent algorithm is used for the optimization of H, θ2DLDA is selected as the initial matrix of θ2DHDA for iterations
Summary
Suppose there are C sample classes, represented by A1, A2 , A3 ,L , Ac respectively. According to equation (1) and (4), if covariance matrix Wi of all sample classes is assumed equal,. For every nonsingular matrix φ ∈ Rl×l , H (φθ2DHDA ) = H (θ2DHDA ). This means that subsequent feature space transformations of the range of c will not affect the value of the criterion. Face images are projected onto the whole , θ2DHDA the most discriminant features are could not extracted, former d column vectors of θ2DHDA are selected as projection axes, the extracted features expressed as where θ2DHDA (:,1: d ) denotes the former d column vectors of matrix of sample A
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