Performance evaluation of incremental training method for face recognition using PCA

Relevance of 'face recognition' (FR) in the modern world requirements is presented as a case of human machine interaction. Physical conditions that influence the face recognition process regarding the facial features, illumination changes and viewing angles etc. are discussed. Face recognition process predominantly depends on machine perception i.e. information through an array of pixels with respect to the facial image. Details of eigenface approach through the involvement of contemporary algebraic and statistical analysis are revisited. Methodology involved in the Principal Component Analysis and advantages of exposing the data to incremental training (using PCA) are discussed. A model for the implementation of IPCA over the face databases is proposed to estimate its performance for the face recognition process. Performance of the present model is studied in the domain of Euclidean distance, decay parameter, recognition rate, eigenvalues and overall computational time. Present IPCA model administered over standard ORL, FERET databases along with that over the JNTU face database with large number of face images revealed relative performance. The merit of present IPCA is inferred through enhanced recognition rate and reduced complexity (in the algorithm), intelligent eigenvectors and lesser computational time. The results are presented in the wake of the body of data available with other methods.