Face recognition is one of the most important and rapidly advanced active research areas of computer science.In spite of the large number of developed algorithms, real-world performance of face recognition has been disappointing. This study enhances invariant recognition of human faces and analysis to improve face verification and identification performance using Active Appearance Model (AAM) for feature extraction with Bayesian classification approach. This paper addressed some of these issues to bring face recognition more closely to being useful for real-life applications. It directed towards the illumination-invariant automatic recognition of faces and analysis to improve face
verification and identification performance.To compare with other feature extraction at the end of the study, an evaluation has been done with an existing face recognition system using AAM algorithm. The experiments performed on part of the FERET color dataset. The result was satisfied with the acceptance rate more than 96%
