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Neural network model for prediction of facial caricature landmark configuration using modified procrustes superimposition method
| Content Provider | Semantic Scholar |
|---|---|
| Author | Sadimon, Suriati Bte Haron, Habibollah |
| Copyright Year | 2015 |
| Abstract | Artificial Neural Network which possessing self-learning ability has shown great promise in addressing problem of learning an artist style in generating facial caricatures. This paper presents Artificial Neural Network model to imitate a particular artist style and to predict a facial caricature configuration for a given original face image (photo). This paper also describes the data preparing process that proposes a modified procrustes superimposition method in deriving the datasets for the neural network model. The experiment is carried out to compare the modified procrustes superimposition method with the original one and to find the appropriate neural network structure that would yield the most accurate prediction results. Different datasets (N1, N2, and N3) derived from the same raw data but using the different method in preparing the dataset and different numbers of hidden nodes (6, 12, 18 and 24) are tested. The experimental result and its detail analysis are given and discussed. It proves that neural network has an ability to predict how the artist exaggerates the original facial feature point. Dataset N2 which use Modified Procrustes Superimposition method and the simple structure of a single hidden layer neural network, in which 6 is the number of hidden node, give the best accuracy of the prediction. |
| Starting Page | 42 |
| Ending Page | 66 |
| Page Count | 25 |
| File Format | PDF HTM / HTML |
| Volume Number | 7 |
| Alternate Webpage(s) | http://home.ijasca.com/data/documents/5IJASCA-070305_Pg42-66_Neural-Network-Model-for-Prediction-of.pdf |
| Journal | SOCO 2015 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
