Manifold Learning for Gender Classification from Face Sequences

We propose a novel approach to gender recognition for cases when face sequences are available. Such scenarios are commonly encountered in many applications such as human-computer interaction and visual surveillance in which input data generally consists of video sequences. Instead of treating each facial image as an isolated pattern and then combining the results (at feature, decision or score levels) as generally done in previous works, we propose to exploit the correlation between the face images and look at the problem of gender classification from manifold learning point of view. Our approach consists of first learning and discovering the hidden low-dimensional structure of male and female manifolds using an extension to the Locally Linear Embedding algorithm. Then, a target face sequence is projected into both manifolds for determining the gender of the person in the sequence. The matching is achieved using a new manifold distance measure. Extensive experiments on a large set of face sequences and different image resolutions showed very promising results, outperforming many traditional approaches.