aspg-2-3045-1791417125 20261007235205 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Cybersecurity and Information Management JCIM 2769-7851 2690-6775 2024 14 2 Faqeda Hassen Kareem College of Science for Women, University of Babylon, Iraq Mohammed Abdullah Naser College of Science for Women, University of Babylon, Iraq Face detection is a crucial aspect of computer vision and image processing, in order to enable the automatic detection and identification of human faces in video streams, face detection is an essential component of computer vision and image processing. Applications for facial recognition, video analytics, security systems, and surveillance all depend on it. Face identification techniques face many obstacles and issues, such as positional fluctuations, illumination changes, resolution and scale issues, facial emotions, and cosmetics. Robust algorithms are required for efficient face detection. This field looks at the feature extraction process using a variety of techniques. These consist of the center symmetric local binary patterns (CS_LBP) approach and the local binary patterns (LBP) method. The YouTube Face database provided the video frames that we used for our study. In order to train the convolutional neural network (CNN) to detect human faces in the video and draw a bounding box around them. The experimental results of the suggested approaches show that. The accuracy rate was 94% higher with the LBP techniques. However, the CS_LBP technique showed the best level of accuracy in both face detection and face rectangle recognition, with an accuracy rate of 95%. 2024 343 351 3045 https://creativecommons.org/licenses/by/4.0/ 10.54216/JCIM.140225 https://www.americaspg.com/journal/2/article/3045 https://www.americaspg.com/storage/11720997621.pdf