Learning-based encoding with soft assignment for age estimation under unconstrained imaging conditions

In this paper we propose to adopt a learning-based encoding method for age estimation under unconstrained imaging conditions. A similar approach [Cao et al., 2010] is applied to face recognition in real-life face images. However, the feature vectors are encoded in hard manner i.e. each feature vector is assigned to one code. The face is divided into patches where a code histogram is built for each patch. However, the codebook is learned using sample features from the entire face. Therefore, we propose an approach to extract robust and discriminative facial features and use soft encoding. Instead of learning a codebook from the entire face, we extract and learn multiple codebooks for individual face patches. The encoding is done by a weighting scheme in which each pixel is softly assigned to multiple candidate codes. Finally, orientation histogram of local gradients in neighborhood has been introduced as feature vector for code learning. On a large scale face dataset which contains 2744 real-life faces, the age group classification using our method achieves an absolute(relative) improvement of 3.6%(6.5%) over the best reported results [Shan, 2010].