Aiming at the problem of low detection rate of depth feature based object detection method Faster R-CNN (Faster Region-based Convolutional Neural Network) in flame detection tasks, a color-guided anchoring strategy was proposed. In this strategy, a flame color model was designed to limit the generation of anchors, which means the flame color was used to limit the generation locations of the anchors, thereby reducing the number of initial anchors and improving the computational efficiency. To further improve the computational efficiency of the network, the masked convolution was used to replace the original convolution layer in the region proposal network. Experiments were conducted on BoWFire and Corsician datasets to verify the detection performance of the proposed method. The experimental results show that the proposed method improves detection speed by 10.1% compared to the original Faster R-CNN, has the F-measure of flame detection of 0.87 on BoWFire, and has the accuracy reached 99.33% on Corsician.The proposed method can improve the efficiency of flame detection and can accurately detect flames in images.
In order to solve the problems, such as facial change and uneven gray, caused by the variations of expression and illumination in face recognition, a novel feature extraction method based on Sub-pattern Row-Column Two-Dimensional Linear Discriminant Analysis (Sp-RC2DLDA) was proposed. In the proposed method, by dividing the original images into smaller sub-images, the local features could be extracted effectively, and the impact of variations in facial expression and illumination was reduced. Also, by combining the sub-images at the same position as a subset, the recognition performance could be improved for making full use of the spatial relationship among sub-images. At the same time, two classes of features which complemented each other can be obtained by synthesizing the local sub-features which were achieved by performing 2DLDA (Two-Dimensional Linear Discriminant Analysis) and Extend 2DLDA (E2DLDA) on a set of partitioned sub-patterns in the row and column directions, respectively. Then, the recognition performance was expected to be improved by employing a fusion method to effectively fuse these two classes of complementary features. Finally, nearest neighbor classifier was applied for classification. The experimental results on Yale and ORL face databases show that the proposed Sp-RC2DLDA method reduces the influence of variations in illumination and facial expression effectively, and has better robustness and classification performance than the other related methods.
Concerning the problem that the linear eigentransformation method cannot capture the statistical properties of the nonlinear facial image, a Data-driven Local Eigentransformation (DLE) method for face hallucination was proposed. Firstly, some samples most similar to the input image patch were searched. Secondly, a patch-based eigentransformation method was used for modeling the relationship between the Low-Resolution (LR) and High-Resolution (HR) training samples. Finally, a post-processing approach refined the hallucinated results. The experimental results show the proposed method has better visual performance as well as 1.81dB promotion over method of locality-constrained representation in objective evaluation criterion for face image especially with noise. This method can effectively hallucinate surveillant facial images.