All Common Subsequences for Face Recognition

Abstract

In recent years, face recognition has become one of the hottest research topics aimed at biometric applications. Comparing with other biometrics recognition, face recognition provides more natural means for perceptual interface. However, face recognition algorithms weakly perform under some common conditions, which include the variation of facial expressions or lightening conditions, the occlusion of faces like wearing glasses or mask, the low resolution or noises of input images, and the like. The other problem is the recognition efficiency, especially when the facial database is tremendous. This paper presents all common subsequences (ACS) as the kernel function (similarity method) to solve the time series problem. Experiments on 4 public face databases: Caltech, Jaffe, Orl and Yale databases, demonstrate that ACS can achieve higher recognition accuracy than some classic face recognition methods, e.g. 2DPCA and 2DLDA.These instructions give you basic guidelines for preparing camera-ready papers for conference proceedings.