Optimal Deep Learning based Convolution Neural Network for digital forensics Face Sketch Synthesis in internet of things (IoT)

Abstract

The rapid development in 5G cellular and IoT technologies is expected to be deployed widespread in the next few years. At the same time, crime rates are also increasing to a greater extent while the investigation officers are held responsible to deal with a broad range of cyber and internet issues in investigations. Therefore, advanced IT technologies and IoT devices can be deployed to ease the investigation process, especially, the identification of suspects. At present, only a few research works has been conducted upon deep learning-based Face Sketch Synthesis (FSS) models, concerning its success in diverse application domains including conventional face recognition. This paper proposes a new IoT-enabled Optimal Deep Learning based Convolutional Neural Network (ODL-CNN) for FSS to assist in suspect identification process. The hyper parameter optimization of the DL-CNN model was performed using Improved Elephant Herd Optimization (IEHO) algorithm. In the beginning, the proposed method captures the surveillance videos using IoT-based cameras which are then fed into the proposed ODL-CNN model. The proposed method initially involves preprocessing in which the contrast enhancement process is carried out using Gamma correction method. Then, the ODL-CNN model draws the sketches of the input images following which it undergoes similarity assessment, with professional sketch being drawn as per the directions from eyewitnesses. When the similarity between both the sketches are high, the suspect gets identified. A comprehensive qualitative and quantitative examination was conducted to assess the effectiveness of the presented ODL-CNN model. A detailed simulation analysis pointed out the effective performance of ODL-CNN model with maximum average Peak Signal to Noise Ratio (PSNR) of 20.11dB, Average Structural Similarity (SSIM) of 0.64 and average accuracy of 90.10%.