A Novel Skin and Mole Pattern Identification Using Deep Residual Pooling Network (DRPN)

Abstract

The process of identifying a criminal is extremely challenging if no eyewitnesses are available or closedcircuit television (CCTV) footage does not show the criminal's face. With the use of high-definition cameras and soft biometrics, forensic science has made great strides in identifying criminals based on their bodily traits. Due to the use of one type of soft biometric for identification, many researchers have focused on this area and have failed to achieve better matching. Despite the fact that similar soft biometrics share many of the same features, one type of soft biometric matching has been insufficient and resulted in inaccurate results. As a result, it becomes difficult to make distinct classifications. Therefore, this study proposes the implementation of a suspect identification system that uses mole pattern analysis and skin tone matching as soft biometrics. The deep residual pooling network is employed to analyze mole patterns, while the histogram equalization method is employed to analyze skin tones. We have also improved the performance of the deep seg-net and the histogram equalization technique by incorporating an aggregator operator and an adaptive pixel-wise noise cancellation filter. The experimental results show that the proposed method attains higher accuracy when the loss rate is gradually decreased. The proposed method attains 89.21% accuracy which is higher than the existing approaches and the efficiency of the proposed method is compared with various texture encoders.