The active appearance models (AAM) are robust in face alignment. We use this method to analyze gesture and motions of faces in Human Machine Interfaces (HMI) for embedded systems (mobile phone, game console, PDA: Personal Digital Assistant). However these models are not only high memory consumer but also efficient especially when the aligning objects in the learning data base, which generate model, are imperfectly represented. We propose a new optimization method based on Nelder Mead Simplex. The Simplex reduces 73\% of memory requirement and improves the efficiency of AAM at the same time. The test carried out on unknown faces (from BioID data base) shows that our proposition provides accurate alignment whereas the classical AAM is unable to align the object.