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Face recognition is a biometric tool for authentication and verification having both research and practical relevance. A facial recognition based verification system can further be deemed a computer application for automatically identifying or verifying a person in a digital image. Varied and innovative face recognition systems have been developed thus far with widely accepted algorithms. The two common approaches employed for face recognition are analytic (local features based) and holistic (global features based) approaches with acceptable success rates. In this paper, we present an intelligent hybrid features based face recognition method which combines the local and global approaches to produce a complete a robust and high success rate face recognition system. The global features are computed using principal component analysis while the local features are ascertained configuring the central moment and Eigen vectors and the standard deviation of the eyes, nose and mouth segments of the human face as the decision support entities of the Generalized Feed Forward Artificial Neural Network (GFFANN). The proposed method's correct recognition rate is over 97%.