Exploration of coarse-to-fine FTIR feature extraction for handlebar grip materials: A common path for forensic examination of microtraces

Abstract

Microtraces are capable of transfer (directly or via some intermediary object) between the items involved in criminal activities. They are inevitably encountered in various crimes and help establish the chain of evidence. Due to the high complexity and diversity of microtraces, it is necessary to build a common approach for different types of microtraces, in order to improve the efficiency of forensic science. Therefore, we chose handlebar grip materials from electric bikes as a typical microtrace, collected 41 highly similar samples with the purpose of discrimination, and achieved the database through Fourier transform infrared spectroscopy (FTIR). Firstly, a forensic strategy for examination of handlebar grip materials was built based on the traces, which was investigated by the manufacturing process and proved by FTIR spectra. Secondly, a coarse-to-fine approach for feature space reduction was performed by a combination of forensic expertise and principal component analysis (PCA). Further, the idea of hypothesis testing was introduced to this project, in order to filter out the possible non-informative variability over the spectral range. Feature annealed independent rules (FAIR) and higher criticism thresholding (HC) were combined with Fisher’s discriminant (LDA) respectively to train the classifier to achieve a coarse-to-fine extraction of features automatically. The performance of FAIR-LDA and HC-LDA was discussed. The results indicated that individual characteristics of handlebar grip materials were presented by weak signal-to-noise ratio in FTIR spectra, which cannot be fully explored by a semi-automatic method based on expert experience. Compared to the coarse extraction of FTIR features by FAIR, HC could filter 99% of invalid features, thus identifying those ‘weak but important’ signals more effectively and significantly. 87.7% of the analysis by HC-LDA can achieve accurate discrimination, which has increased 22.5% and 3.1% from that of LDA and FAIR-LDA respectively. HC integrated with LDA improves the accuracy of individual discrimination by its sparse sensitive discriminant features extraction. The present work demonstrates an accurate method for FTIR feature extraction as well as a scientific basis for forensic examination of microtraces.