Face recognition is a biometric approach which can extract the facial characteristics of a person without his/her cooperation. The face recognition system fails to identify a person after some years because of the age related variations shown on the face. The face recognition system should be able to handle such age related variations and recognise the person irrespective of his age. The aging variations on a face are seen in the form of wrinkles, shape changes etc. The process of aging is highly composite and its effects on face are unpredictable. The aging effects are unique to every individual's face. In this paper we try to model the age variations of face using a self -Principal Component Analysis (PCA) based method. We have compared the recognition rate with the conventional PCA and an improved recognition rate is obtained using our method.

Objectives: To find the optimal allocation of resources and minimize the overall cost while meeting the performance requirements of the applications. Methods: The proposed Intelligent PSO-based resource optimization in cloud computing evaluates the quality of the solutions based on their resource allocation parameters. Cloud Sim software is used as a simulation tool for testing and evaluating new solutions and strategies in the cloud. The Closest Data Center Service Broker Policy is implemented in Cloud Simulation. Findings: The proposed technique assigns workloads effectively on available resources with an improvement of 10.46% in electricity consumption. Novelty:The algorithm can further be employed for identifying the unused VM's in the Data Center to reduce the cost.