Deep feature selection and projection for cross-age face retrieval

While traditional PIE (pose, illumination and expression) face variations have been well settled by latest methods, a new kind of variation, cross-age variation, is drawing attention from researchers. Most of the existing methods fail to maintain the effectiveness in real world applications that contain significant gap of age. Cross-age variation is caused by the shape deformation and texture changing of human faces while people getting old. It will result in tremendous intra-personal changes of face feature that deteriorate the performance of algorithms. This paper proposed a deep feature based framework for face retrieval problem. Our framework uses deep CNNs feature descriptor and two well designed post-processing methods to achieve age-invariance. To the best of our knowledge, this is the first deep feature based method in cross-age face retrieval problem. The deep CNNs model we use is firstly trained on traditional PIE datasets and then fine-tuned by cross-age dataset. The feature selection and projection post-processing we propose is also proved to be very effective in eliminating cross-age variation of deep CNNs feature. The experiments conducted on Cross-Age Celebrity Dataset (CACD), which is the largest public dataset containing cross-age variation, show that our framework outperforms previous state-of-the-art methods.