Acquisition of Face Depth Information from Near Infrared Images
Ying Zheng, Stan Ziqing Li, Jianglong Chang, Zengfu Wang · 2007
This paper proposes a method for acquiring face depth information directly from near infrared (NIR) images, using statistical learning. To perform such learning, ground truth NIR images and range data are captured. A method of alignment between the two image modalities is proposed. By constructing the low dimensional face subspaces of NIR images and depth maps, the raw data are projected into respective subspaces. The mapping between the two subspaces is learned. The experiment substantiates the accuracy of the depth recovered and the economy of time and memory consumed.