Low Resolution Face Recognition with Photon-counting Linear Discriminant Analysis
Seokwon Yeom · Journal of the Institute of Electronics Engineers of Korea · 2008
This paper discusses low resolution face recognition using the photon-counting linear discriminant analysis (LDA). The photon-counting LDA asymptotically realizes the Fisher criterion without dimensionality reduction since it does not suffer from the singularity problem of the fisher LDA. The linear discriminant function for optimal projection is determined in high dimensional space to classify unknown objects, thus, it is more efficient in dealing with low resolution facial images as well as conventional face distortions. The simulation results show that the proposed method is superior to Eigen face and Fisher face in terms of the accuracy and false alarm rates.