Large Scale Face Data Purification based on Correlation Function and Multi-Phase Grouping

Recent advances in deep learning technologies enable high performance artificial intelligence, which is an equivalence of human capability or higher for various application. However, deep learning is highly resorted to the large scale training data, which typically contains large number of outlier samples that are difficult to remove. In this paper, we proposed a face image purifying algorithm, which combines the correlation function of deep features with multi-phase grouping technique. A correlation function was proposed to determine the principal class by measuring the similarities between all different samples. The principal class was further used as a prior for the multi-phase grouping algorithm to purify the face data by multiple thresholds. The experimental results demonstrate that the proposed algorithm has significant improvement than the primitive cluster algorithm, such as K-Means.