We present a novel method for face gender classification problem. This method employs the powerful Active Appearance Model (AAM) method for modeling human faces and extracting feature vectors, and the simple but powerful Fuzzy k-Nearest Neighbors (Fuzzy k-NN) method for classification. Experiments for the proposed approach have been conducted on FERET data set and the results show that the proposed method could improve the classification rates.
|Title of host publication||Proceedings Of The 2012 International Conference On Image Processing, Computer Vision, & Pattern Recognition (IPVC 2012) : Vol. 2|
|Editors||Hamid R Arabnia, Leonidas Deligiannidis|
|Place of Publication||USA|
|Number of pages||5|
|ISBN (Print)||9781601322232, 1601322240, 1601322259|
|Publication status||Published - 2012|
|Event||16th International Conference on Image Processing, Computer Vision & Pattern Recognition - Las Vegas, Las Vegas, United States|
Duration: 16 Jul 2012 → 19 Jul 2012
|Conference||16th International Conference on Image Processing, Computer Vision & Pattern Recognition|
|Abbreviated title||IPCV 2012|
|Period||16/07/12 → 19/07/12|
Bui, L., Tran, D., Huang, X., & Chetty, G. (2012). Face gender classification based on active appearance model and fuzzy k-nearest neighbors. In H. R. Arabnia, & L. Deligiannidis (Eds.), Proceedings Of The 2012 International Conference On Image Processing, Computer Vision, & Pattern Recognition (IPVC 2012) : Vol. 2 (Vol. 2, pp. 617-621). USA: CSREA Press.