A novel lossless compression framework for facial depth images in expression recognition

With the development of AR and VR, depth images are widely used for facial expression analysis and recognition. To reduce the storage size and save bandwidth, an efficient compression framework is desired. In this paper, we propose a novel lossless compression framework for facial depth images in expression recognition. In the proposed framework, two steps are designed to remove the redundancy in the facial depth images, which are data preparing and bitstream encoding operations. In the data preparing operation, the original image is represented by the same and different parts between the left and right sides. In the bitstream encoding operation, these parts are compressed to get the final bitstream. The proposed framework is implemented and examined on the BU-3DFE Database. Experimental result shows that the proposed technique outperforms existing lossless compression frameworks in terms of compression efficiency, and the average data size is reduced to 25.27% by the proposed framework.