Light field imaging based on a parallel SVM method for recognizing 2D fake pedestrians

Abstract:

It is novel to apply three-dimensional (3D) light field imaging technology to recognize two-dimensional (2D) fake pedestrians. In this paper, we propose a parallel support vector machine (SVM) method based on 3D light field imaging (light field camera) and machine learning techniques. A light field (LF) camera with robust sensors, which is able to record rich 3D information, is used as hardware equipment. Histogram of oriented gradient (HOG) feature extraction algorithm and SVM classification method are used to recognize the real and 2D fake pedestrians efficiently. Besides, we carry out an experiment on our improved LF pedestrian dataset. The experimental results of parameter optimization study show that in the case of 400 training samples (200 positive samples and 200 negative samples), 120 to 420 testing samples, and an HOG cellsize as 8×8, the best recognition accuracy with polynomial kernel function is improved by more than 2% compared with the previous method. The best accuracy is 99.17%. Otherwise, the recognition accuracy of more than 98.00% will be obtained even under other experimental conditions.