AN INTEGRATED APPROACH TO EDGE DETECTION AND FACE IDENTIFICATION ALGORITHMS FOR ENHANCED VISUAL RECOGNITION

Visual recognition plays a crucial role in many applications, ranging from surveillance systems to social media platforms. However, the accuracy and efficiency of current visual recognition systems are often compromised due to insufficient edge detection and face identification techniques. In this paper, we propose an integrated approach that combines advanced edge detection and face identification algorithms to improve visual recognition. Our method involves a two-stage process, with the first stage utilizing the Canny algorithm to focus on edge detection, and the second stage using a convolutional neural network (CNN) for face identification. We tested our approach on multiple datasets and demonstrated a higher accuracy rate in visual recognition compared to existing techniques. Additionally, the integrated approach proved to be robust against variations in lighting conditions and facial expressions. Our proposed method enhances the accuracy and efficiency of visual recognition systems, which could lead to more reliable and robust applications in various domains. Nevertheless, this integrated approach raises concerns about privacy and data security that require further exploration.