Efficient bi-traits identification using CEDRNN classifier for forensic applications

Abstract

The evidence, traits, in addition to clues that are amassed as of the Crime Scenes (CS) are analyzed by a process termed Forensic application. Nevertheless, the Forensic Analysis (FA) faces the challenges of irrelevant Feature Extraction (FE), which is because of the over impression of Finger-Print (FP), facial expressions, pose-invariant, together with poor lightings that bring about the mis-identification of the offender. To trounce such challenges, this paper proposes a bi-traits-based offender identification using Cross Entropy-based Deep Remainder Neural Network (CEDRNN) for forensic application. Pre-processing, FE, Feature Selection (FS), together with identification are ‘4′ phases that the proposed model encompasses. In the pre-processing phase, the input data is processed via modifying the image size, augmenting the Image Contrast (IC), and neglecting the unwanted data. Then, as of the pre-processed data, the more apt and helpful data are extracted in FE. Then, the Gaussian Sailfish Optimization (GSO) technique selects the important data in the FS phase. Finally, the CEDRNN identifies the offender centered on the selected features. In this research, publically available datasets are utilized for comparing the outcomes of the CEDRNN with the previous top-notch algorithms. The experimental outcomes exhibited that the CEDRNN attains the highest accuracy and also effectively identifies the offender without mis-prediction.