Face Spoofing Detection by Fusing Binocular Depth and Spatial Pyramid Coding Micro-Texture Features

Robust features are of vital importance to face spoofing detection, because various situations make feature space extremely complicated to partition. Thus in this paper, two novel and robust features for anti-spoofing are proposed. The first one is a binocular camera based depth feature called Template Face Matched Binocular Depth (TFBD) feature. The second one is a high-level micro-texture based feature called Spatial Pyramid Coding Micro-Texture (SPMT) feature. Novel template face registration algorithm and spatial pyramid coding algorithm are also introduced along with the two novel features. Multi-modal face spoofing detection is implemented based on these two robust features. Experiments are conducted on a widely used dataset and a comprehensive dataset constructed by ourselves. The results reveal that face spoofing detection with the fusion of our proposed features is of strong robustness and time efficiency, meanwhile outperforming other state-of-the-art traditional methods.Comment: 5 pages, 2 figures, accepted by 2017 IEEE International Conference on Image Processing (ICIP

Credit Risk Migration Analysis of Illinois Farm Business: Possible Impacts of Farm Business Cycle

This study uses the cohort approach to estimate the credit risk migration probability of farm business. Using data from the Farm Business and Farm Management, this study rates the credit risk into 10 risk levels plus a default level, defines a farm business cycle with peak, normal and trough periods and evaluates the effect on farm financial performance of the farm business booms and slumps. The results show that the farms with low credit risk are more likely to stay in the same risk level but the farms with high credit risk have the trend to improve their risk situation and move upwards. The results also show that the credit risk ratings are more likely to move upgrade during farm business cycle peaks.Agricultural Finance,