A Real-Time Hand Sign Language Recognition System for Threatening Situations Using Deep Learning - Shwany, Zardasht Abdulaziz Abdulkarim; Ismail, Shayda Khudhur; Khoshnaw, Karwan Hoshyar Khalid; Mustafa, Twana Saeed; Karabatak, Murat; Othman, Nashwan Adnan (Institute of Electrical and Electronics Engineers Inc., 2024)
Hand sign language has been done as physical movements in natural languages that humans have used since ancient times, along with letters, words, and spoken language. This paper presents a real-time method to identify threatening signs by criminals during interrogation, which increases their criminal rank and helps to reach conclusions. The proposed method is to install a camera of appropriate quality in front of the offender, record hand gestures in a specific area of the hand, apply some image processing techniques, such as contrast enhancement techniques, to the image to facilitate recognition as input, and then classify the images using a convolutional neural network (CNN) for a specific problem with high specifications for that topic by and using AlexNet. The accuracy of the presented method is more than 94.2% in real-time testing. © 2024 IEEE.
12th International Symposium on Digital Forensics and Security, ISDFS 2024 -- 29 April 2024 through 30 April 2024 -- San Antonio -- 199532
Keywords: CE Techniques; CNN; Deep Learning; Hand Gesture; Real-time