No single subspace feature extraction algorithm outperforms others under all circumstances

but applying dual space feature extraction algorithm can overcome the limits of single subspace. To increase the recognition rate of classified results

a new dual space feature extraction algorithm is put forward based on PCA (principal component analysis) and ICA (independent component analysis). The algorithm uses ICA as PCA’s complementary space to realize feature extraction. The aim of the algorithm is to reproject the samples into ICA subspace to classify

which are difficult to be classified in PCA subspace. The proposed dual space extraction algorithm includes two steps. Step 1 is called preclassify

which uses two classifiers in a single subspace to classify the testing samples. If a testing sample is classified into different classes by two classifiers

it would be added into the new testing samples set. In step 2

the algorithm projects the samples of the new testing samples set into another subspace and classifies them. Then

the recognition results of two subspaces are tested together. The experimental results in ORL and FERET database prove the dual space feature extraction algorithm outperforms the traditional feature extraction algorithm in recognition rate.