Infrared Face Recognition Combining DCT and Features Selection
Xie Zhi-hu · Jisuanji kexue yu tansuo · 2014
To get the good performance of infrared face recognition from the biological feature and statistical character,this paper proposes a novel method for infrared face recognition based on blood perfusion image by combining discrete cosine transform(DCT) and features selection. Firstly, infrared thermal images are converted into blood perfusion domain by blood perfusion model to get the constant information. Secondly, DCT is chosen to reduce the correlation in original face image. Finally, to improve the effectiveness of features extraction in DCT domain, the objectives of features selection and subspace learning are consistent(both follow the separability discriminant criterion): a feature selection algorithm is proposed to extract the DCT coefficients, LDA(linear discriminant analysis) is applied to DCT coefficients extracted by the feature selection algorithm. The experimental results illustrate that the proposed method can quickly and efficiently extract the features of blood perfusion domain for classification, and get better recognition performance than traditional DCT+LDA method.