The proposed work aims at identifying and greeting people. A new technique is introduced that incorporates the facial features and KNN classifier. Since human face is the one which is said to be the most representative, the features of the face (eyes, nose and mouth) are extracted and are used for training the classifier. The proposed frame work consists of four phases: Facial Features Detection (FFD), Detected Features Positioning (DFP), Descriptive Features Extraction (DFE) and Face Identification (FI). The proposed algorithm can be used in a wide variety of scenarios such as campus, office etc. after being trained with the corresponding dataset. The performance of the system is analyzed for various scenarios. Good average accuracy of 96.05% has been achieved. © 2018, Springer International Publishing AG.
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V. Pranav, Manjusha, R., and Latha, P., “Design of an algorithm for people identification using facial descriptors”, Lecture Notes in Computational Vision and Biomechanics, vol. 28, pp. 1117-1128, 2018.