Gender classification of depth images based on shape and texture analysis
Xiaolong Wang, Chandra Kambhamettu · 2013
Gender classification of depth images is a challenging problem, most research work attempted to use shape information to solve this problem in the past literature. In this work, we propose a new fusion scheme for gender classification using both texture and shape features. A new ensemble scheme is advocated to combine texture and shape feature at the feature level. To evaluate the performance of our algorithm, we measure our scheme on two different datasets. The final classification result is up to 93.7% using five-fold cross validation on the whole FRGCv2 dataset, which is comparable to the classification result obtained using visible imagery.