Fired bullet signature correlation using the finite ridgelet transform (FRIT) and the gray level co-occurrence matrix (GLCM) methods. (January 2022)

• Record Type: Journal Article
• Authors: Zhu, Jialing; Hong, Rongjing; Zhang, Hao; Gu, Ran; Wang, Hua; Sun, Fuzhong
• Is Part Of: Forensic science international. Volume 330(2022)
• Publication Date: 2022-01
• Subjects: Automated ballistic identification -- Land engraved areas (LEA) images -- Striation marks -- Linear weighted average profile -- Valid correlation areas; Medical jurisprudence -- Periodicals; Chemistry, Forensic -- Periodicals; Forensic Medicine -- Periodicals; Médecine légale -- Périodiques; Chimie légale -- Périodiques; Gerechtelijke geneeskunde; Gerechtelijke chemie; Gerechtelijke psychiatrie; Chemistry, Forensic; Medical jurisprudence; Electronic journals; Periodicals; Electronic journals; 614.1
• DOI: 10.1016/j.forsciint.2021.111089
• Languages: English
• ISSNs: 0379-0738

Abstract

ABstract: When a bullet is fired from a barrel, micro striation marks caused by the sliding motion of the bullet through the rifled barrel are one of the foremost factors in automated ballistic identification. This paper focuses on 3D topography images of land engraved areas (LEA) and proposes a bullet identification method incorporating the finite ridgelet transform (FRIT) and gray level co-occurrence matrix (GLCM) algorithms. The FRIT extracts the striation marks from the 3D micro image and the GLCM generates a linearly weighted weight corresponding to the texture features for 2D average profile calculation. The entire striation marks image is divided into several cells and a cell with valid correlation areas is assigned a large weight, but the one with invalid correlation areas is assigned a small weight along the vertical direction. The visible results show that the valid correlation areas are effectively identified and the negative effects of invalid correlation areas are suppressed. Tests were performed on a control set and an unknown set, giving a total of 35 bullet samples fired from pistols with 10 consecutively manufactured slides. The results included no false identifications or false exclusions and a clear separation between the matching index of the matching and non-matching LEA profiles, demonstrating excellent performance in striation mark capture and valid correlation areas extraction of FRIT and GLCM algorithms. The proposed method is capable of correctly matching toolmarked surfaces to the barrel used. Highlights: An automated identification method for striation marks on LEA of fired bullets. The FRIT method is implemented to extract effective striation marks. The GLCM method gives a linearly weighted weight for 2D average profile calculation. The GLCM method is exploited to reduce the potential impact of invalid correlation areas. The proposed method successfully separates matching and non-matching samples.