摘要
探究了高光谱成像结合多种模型对玻璃上汗潜指纹遗留时间的预测.采集玻璃上汗潜指纹的高光谱图像数据,对原始光谱数据进行Savitzky-Golay(SG)卷积平滑和标准正态变换预处理,通过连续投影算法选取特征波段,构建并对比了支持向量机(SVM)、遗传优化反向传播(GA-BP)神经网络和偏最小二乘回归(PLSR)三种模型在全波段及特征波段情况下对玻璃上汗潜指纹遗留时间的预测效果.结果表明三种模型在全波段下都无法适用,在特征波段下,SVM、GA-BP神经网络、PLSR三种模型的预测均方根误差分别为 3.247 d、3.035 d、3.060 d,决定系数达到 0.627、0.659、0.606,相对分析误差均高于1.4,可在一定程度上对指纹的遗留时间进行预测.可见高光谱成像技术结合多种模型可用于预测玻璃上汗潜指纹的遗留时间.
Abstract
This study explores the prediction of latent sweat fingerprint retention time on glass using hyperspectral imaging combined with multiple models.The hyperspectral image data of latent sweat fingerprints on glass were collected,and Savitzky-Golay(SG)convolutional smoothing and standard normal variate transformation were performed on the original spectral data.The feature bands were selected using successive projections algorithm,and then support vector machine(SVM),genetic algorithm back propagation(GA-BP)neural network,and partial least squares regression(PLSR)models were constructed and compared for predicting the latent sweat fingerprint retention time on glass in both full and feature bands.The results indicate that these three models are not applicable in the full band.In the feature band,the values of root mean square error of prediction of SVM,GA-BP neural network,and PLSR models reached 3.247 d,3.035 d,and 3.060 d,respectively,with coefficient of determination reaching 0.627,0.659,and 0.606,respectively.The relative percent deviation is higher than 1.4 with all the three models,thus predicting the retention time of fingerprints to a certain extent.Notably,hyperspectral imaging technology combined with multiple models can be used to predict the retention time of sweat latent fingerprints on glass.
基金项目
中国刑事警察学院研究生创新能力提升项目(2023YCYB49)
NETL
NSTL
万方数据