Tightly-Coupled Data Compression for Efficient Face Alignment

Abstract

Face alignment is the key component for applications such as face and expression recognition, face based AR (Augmented Reality), etc. Among all the algorithms, cascaded-regression based methods have become popular in recent years for their low computational costs and satisfactory performances in uncontrolled environments. However, the size of the trained model is large for cascaded-regression based methods, which makes it difficult to be applied in resource restricted scenarios such as applications on mobile phones. In this paper, a data compression method for the trained model of supervised descent method (SDM) is proposed. Firstly, according to the distribution of the model data estimated with the non-parametric method, a K-means based data quantization algorithm with probability density-aware initialization was proposed to efficiently quantize the model data. Then, a tightly-coupled SDM training algorithm was proposed so that the training process reduced the errors caused by data quantization. Quantitative experimental results proved that our proposed method compressed the trained model to less than 19% of its original size with very similar feature localization performance. The proposed method opens the gates to efficient mobile face alignment applications based on SDM.

Keywords

• model compression
• tightly-coupled training
• feature localization