Document Type
Oral Presentation
Campus where you would like to present
Ellensburg
Event Website
http://digitalcommons.cwu.edu/source/
Start Date
18-5-2016
End Date
19-5-2016
Keywords
Deep learning, gender classification
Abstract
Building software that can visually and accurately perceive gender from face images is an important step in making more intelligent machines. Several approaches to this problem have been suggested in the literature. We evaluate Histogram of Oriented Gradients, Dual Tree Complex Wavelet Transform (DTCWT) Principal Component Analysis (PCA) with Support Vector Machines (SVM) and compare them to Convolutional Neural Networks for this task. We train and test our classifiers with two benchmarks containing thousands of facial images. As expected, convolutional neural networks had the best performance while the performance of DTCWT varied most depending on the dataset used
Recommended Citation
Lemley, Joseph; Abdul-Wahid, Sami; and Banik, Dipayan, "Automatic classification of perceived gender from face images" (2016). Symposium Of University Research and Creative Expression (SOURCE). 2.
https://digitalcommons.cwu.edu/source/2016/cos/2
Department/Program
Computational Science
Automatic classification of perceived gender from face images
Ellensburg
Building software that can visually and accurately perceive gender from face images is an important step in making more intelligent machines. Several approaches to this problem have been suggested in the literature. We evaluate Histogram of Oriented Gradients, Dual Tree Complex Wavelet Transform (DTCWT) Principal Component Analysis (PCA) with Support Vector Machines (SVM) and compare them to Convolutional Neural Networks for this task. We train and test our classifiers with two benchmarks containing thousands of facial images. As expected, convolutional neural networks had the best performance while the performance of DTCWT varied most depending on the dataset used
https://digitalcommons.cwu.edu/source/2016/cos/2
Faculty Mentor(s)
Dr. Razvan Andonie