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Classification or typology systems used to categorize different human body parts have existed for many years. Nevertheless, there are very few taxonomies of facial features. Ergonomics, forensic anthropology, crime prevention or new human-machine interaction systems and online activities, like e-commerce, e-learning, games, dating or social networks, are fields in which classifications of facial features are useful, for example, to create digital interlocutors that optimize the interactions between human and machines. However, classifying isolated facial features is difficult for human observers. Previous works reported low inter-observer and intra-observer agreement in the evaluation of facial features. This work presents a computer-based procedure to automatically classify facial features based on their global appearance.
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Facial Feature Recognition using Neural Networks

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Facial Features Set – PCF Studios

Jonathon, et al. IEEE, Locality Preserving Projections. Cohn, and Yingli Tian. Kirchberg, and Robert W. Springer Berlin Heidelberg, Labeled faces in the wild: A database for studying face recognition in unconstrained environments.
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Automatic classification of human facial features based on their appearance

All relevant data are within the paper. Classification or typology systems used to categorize different human body parts have existed for many years. Nevertheless, there are very few taxonomies of facial features. Ergonomics, forensic anthropology, crime prevention or new human-machine interaction systems and online activities, like e-commerce, e-learning, games, dating or social networks, are fields in which classifications of facial features are useful, for example, to create digital interlocutors that optimize the interactions between human and machines. However, classifying isolated facial features is difficult for human observers.
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Facial Feature Recognition using Neural Networks In the Fall of , for a class project in Artificial Intelligence, I designed a neural network to locate facial features in images. The one hundred images I used came from the underclassmen section of the University High School yearbook. They were scanned in at 96 by resolution. I set four of the images aside to comprise the testing set, and for the remaining ninety-six I manually specified the coordinates of the left eye, right eye, nose, and mouth.
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