Kernel ELM and CNN Based Facial Age Estimation
We propose a two level system for apparent age estimation from facial images. Our system first classifies samples into overlapping age groups. Within each group, the apparent age is estimated with local regressors, whose outputs are then fused for the final estimate. We use a deformable parts model based face detector, and features from a pretrained deep convolutional network. Kernel extreme learning machines are used for classification. We evaluate our system on the ChaLearn Looking at People 2016 Apparent Age Estimation challenge dataset, and report 0.3740 normal score on the sequestered test set.
2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)