LSB steganography strengthen footprint biometric template

Abstract

Steganography is the science of hiding secret data inside another data type as image and text. This data is known as carrier data; it lets people interconnect secretly. This suggested paper aims to design a Steganography Biometric Imaging System (SBIS). The system is constructed in a hybridization manner between image processing, steganography, and artificial intelligence techniques. During image processing techniques the system receives RGB foot-tip images and preprocesses the images to get foot-template images. Then a chain code is illustrated for personal information within the foot-template image by Least Significant Bit (LSB). Accurate recognition operation is performed by artificial bee colony optimization (ABC). The automated system was tested on a live-took about ninety RGB foot-tip images known as the cover image and clustered to nine clusters that authorized visual database. The Least Significant Bit method transforms the foot template to a stego image and is stored on a stego visual database for further use. Features database was constructed for each stego footprint template. This step converts the image to quantities data and stored in an Excel feature database file. The quantities data was used at the recognition stage to produce either a notification of rejection or acceptance. At the acceptance choice, the corresponding stego foot-tip template occurrence was retrieved, it is corresponding individual data were extracted and cluster position on the stego template visual database. Indeed, the foot-tip template is displayed. The suggested work consequence is affected by the optimum feature selection via the artificial bee colony optimization usage and clustering, which declined the complication and subsequently raised the recognition rate to 93.65 %. This rate competes out the technique over others’ techniques in the field of biometric recognition

Classification of Lateral Incisor Teeth for Aesthetic Dentistry Using Intelligent Techniques

“…where tp is the number of images that have been appropriately identified, tn is the number of images that have been unequivocally rejected, and N is the total number of records in the database. The recall is the number of true positive cases for which the forecast was accurate, as demonstrated in equation ( 3) [23,24].…”