Hair Patterns and Skin Marks for Criminal and Victim Identification using RPPSM Approach

Abstract

Identifying criminals and victims in images (e.g., child pornography and masked gunmen) can be a challenging task, especially when neither their faces nor tattoos are observable. Blood vessel patterns are recently proposed to address this problem. However, they are invisible in low-resolution images and dense androgenic hair can cover them completely. Medical research results have implied that androgenic hair patterns are a stable biometric trait and have potential to overcome the weaknesses of blood vessel patterns. To the best of our knowledge, no one has studied androgenic hair patterns and skin marks for criminal and victim identification before. Identify criminals through androgenic hair patterns and skin marks in low resolution images using “Relatively Permanent Pigmented Skin Marks” abbreviated as RPPSM. An algorithm to compute feature extraction of androgenic hair patterns, a biometric trait composed of a group of skin marks are nevi, lentigious. Clustering algorithm automatically group similar colors in our image in the database. K-Means clustering algorithm uses the RGB values and height and width of the image .KNN clustering algorithm is used to identification of criminal and victim through hair pattern, skin marks and clustering process.