Gender classification based on integration of multiple classifiers using various features of facial and neck images

To reduce the rate of error in gender classification, we propose the use of an integration framework that uses conventional facial images along with neck images. First, images are separated into facial and neck regions, and features are extracted from monochrome, color, and edge images of both regions. Second, we use Support Vector Machines (SVMs) to classify the gender of each individual feature. Finally, we reclassify the gender by considering the six types of distances from the optimal separating hyperplane as a 6-dimensional vector. Experimental results show a 28.4% relative reduction in error over the performance baseline of the monochrome facial image approach, which until now had been considered to have the most accurate performance.