Two-Layered Face Detection System using Evolutionary Algorithm
Jun-Su Jang, Jong-Hwan Kim · 2005
This paper proposes a novel face detection method based on principal component analysis (PCA) and evolutionary algorithm (EA). In a view-based approach to face detection, the face is treated as an input vector of high dimension; and a multivariate Gaussian model is often employed in representing these faces. A near-face is a vector which, according to certain specified distance measure, is close to being a face. EA is employed to estimate the covariance matrix of this model, which discriminates between face class and near-face class. The proposed face detection system is characterized by EA-based two-layered classifier which are designed with a cascade structure for efficient performance and computation. The performance of the proposed method is experimentally verified on BioID face database.