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Face Recognition as a biometric tool for identification and verification of persons has gained momentum and practical vitality in the wake of increased and growing security concerns. A facial recognition and face verification system can be considered a computer application for automatically identifying or verifying a person in a digital image in as much as the processing is carried out on digital still facial images. Varied and innovative face recognition systems have been developed thus far with widely accepted algorithms. In this paper, we present an innovative feature extraction method for intelligent local face recognition, configuring the extracted features as the decision support entities of the Generalized Feed Forward Artificial Neural Network (GFFANN). The extracted features reckon in both the geometrical features since the landmarks segments of the face such as the eyes, nose and mouth bear significant information about the facial identity of a person. The proposed method's correct recognition rate is over 95%.