多尺度分区统一化LBP算子井下人员人脸识别方法
Underground personnel face recognition method based on multi-scale unified LBP operator
-
摘要: 针对传统的人脸识别算法在对井下人员识别中易受光照和粉尘干扰，难以适应井下复杂环境，影响识别精度的问题，提出基于多尺度分区统一化LBP算子对井下人员面部进行识别，该方法保留了统一化LBP算子的优点，利用多尺度分区的概念对传统的LBP算子进行扩展，通过对不同尺度下人脸面部图像特征提取，增强了对面部图像的全局及局部把握，削弱了局部噪声对整体识别精度的影响。采用分区处理直方图得到复合的特征向量，弥补了传统算法的单一性，获得了多尺度的直方图特征向量，在图像分类中，利用卡方检验对面部特征直方图相似度进行判断，根据设定的阈值将采集到的图像的特征向量与系统中存储的待匹配的图像的特征向量进行对比，实现井下人员面部识别。试验结果表明：通过多尺度分区统一化LBP算子处理，经过直方图均衡化后的井下人员面部图像纹理特征信息得到充分且有效的提取，可以全面地反映井下人员的面部特征；与传统的井下人员面部识别算法相比，多尺度分区统一化LBP算子对噪声和光照的鲁棒性有了明显提高，结合图像分区，多尺度统一化LBP算子对图像局部和整体的把握能力取得了较好的效果，该方法对井下人员人脸识别准确率达94.25%，比传统的井下面部识别算法提高了5%，提升了井下人员识别精度。Abstract: Aiming at the problem that the traditional face recognition algorithm is vulnerable to illumination and dust in the identification of underground personnel, it is difficult to adapt to the complex environment of underground and affect the recognition accuracy. It is proposed to identify the face of underground personnel based on multi-scale partitioned LBP operator. The method retains the advantages of unified LBP operator, and extends the traditional LBP operator by using the concept of multi-scale partitioning. By extracting facial image features at different scales, the global and local grasp of facial images is enhanced and weakened. The effect of local noise on the overall recognition accuracy. Using the partition processing histogram to obtain the composite feature vector, the singularity of the traditional algorithm is compensated, and the multi-scale histogram feature vector is obtained. In the image classification, the similarity of the facial feature histogram is judged by the chi-square test. The threshold value compares the feature vector of the acquired image with the feature vector of the image to be matched stored in the system to realize face recognition of the underground personnel. The experimental results show that the LBP operator is unified by multi-scale partitioning, and the texture feature information of the facial image of the underground personnel after the histogram equalization is fully and effectively extracted, which can comprehensively reflect the facial features of the underground personnel; Compared with the human face recognition algorithm, the multi-scale partitioning unified LBP operator has significantly improved the robustness of noise and illumination. Combined with image partitioning, the multi-scale unified LBP operator has better grasp of the local and overall image. Good results, the proposed method in the face recognition of the underground personnel, the accuracy rate of 94.25%, compared with the traditional well below recognition algorithm increased by 5%, this method can improve the recognition accuracy in the identification of underground personnel.
-
-
期刊类型引用(13)
1. 余国锋，韩云春，朱文，贺世芳，任波，程庆和. 基于微震监测的深部下分层巷道掘进围岩破坏特征研究. 煤炭技术. 2024(06): 48-51 . 
百度学术
2. 赵文卓，郤保平，韩福芝，樊强，贾毅超. 特厚煤层综放开采沿空掘巷煤柱宽度留设研究. 中国煤炭. 2024(12): 140-147 . 百度学术
3. 经来旺，彭绍驰，李树文，李学帅，经纬. 中间岩层和节理弱面厚度对上行开采的影响. 煤炭技术. 2023(02): 1-4 . 百度学术
4. 林海飞，王旭，徐培耘，孔祥国，双海清，赵鹏翔. 特厚煤层开采卸压瓦斯储集区演化特征分析及工程应用. 煤炭科学技术. 2023(02): 173-182 . 本站查看
5. 卢方超，张学博，高建良. 倾斜特厚煤层上分层开采时下分层煤体载荷及渗透率演化规律研究. 矿业安全与环保. 2023(02): 14-20 . 百度学术
6. 金宁平，付宝杰. 厚煤层分层采动直覆砂岩运移规律研究. 矿业研究与开发. 2023(05): 43-49 . 百度学术
7. 王君. 神达晋保煤矿13101综采工作面采煤设计. 能源与节能. 2023(05): 190-192 . 百度学术
8. 马腾辉，李蓉，王坤，渠俊峰. 时间序列下煤矸石充填复垦耕地和林地的土壤碳动态特征. 煤炭科学技术. 2023(05): 260-268 . 本站查看
9. 刘振，张智，陈竹. 分层开采下分层窄煤柱沿空掘巷技术应用研究. 矿业研究与开发. 2023(10): 15-20 . 百度学术
10. 王全征. 综放开采工作面下覆煤层采空区探测及充填加固技术研究. 中国矿山工程. 2023(06): 40-44+50 . 百度学术
11. 王鹏，王志强，洛锋，苏越，李敬凯，武超，张智尧. 特厚煤层残留煤柱应力分布与巷道跨煤柱布控技术. 煤炭科学技术. 2023(12): 232-242 . 本站查看
12. 戴文琦. 相邻综采工作面采空区覆岩压力分布特征研究. 煤矿安全. 2022(07): 195-200 . 百度学术
13. 郭明功，王彬彬，刘思博，张文琦. 深部缓倾斜煤层“以孔代巷”抽采技术. 西安科技大学学报. 2022(06): 1071-1079 . 百度学术
其他类型引用(10)
下载:
图(7)
计量
- 文章访问数: 263
- HTML全文浏览量: 0
- PDF下载量: 259
- 被引次数: 23
