Gabor Boost Linear Discriminant Analysis for face recognition

Dong Li, Xudong Xie, Qionghai Dai, Zhigang Jin

Abstract: This paper proposes an innovative algorithm named Gabor Boost Linear Discriminant Analysis (GBLDA) for face recognition. In our method, we want to estimate the distribution of high dimensional Gabor wavelet (GW) features in a low dimensional LDA subspace without computing the GW feature of an input image. The computational complexity can be reduced significantly. Hence, GBLDA is suitable for real-time applications. Experimental results show that our proposed method not only possesses the advantages of linear subspace analysis approaches such as low computational complexity, but also has the advantage of a high recognition performance in the Gabor based methods.