Digital Image Forensics Using Deep Learning and Person Identification

In the realm of computer vision and image processing, the task of digital image identification holds significant importance across various domains. This paper presents a comparative study of two distinct methodologies for digital image identification: deep learning and the Haar cascade algorithm. Deep learning, specifically Convolutional Neural Networks (CNNs), has emerged as a powerful tool for automatically learning hierarchical representations from data and has achieved remarkable success in image-related tasks. In contrast, the Haar cascade algorithm, a classic machine learning technique, offers real-time object detection capabilities with its efficient feature-based approach. Through a series of experiments and evaluations on benchmark datasets, we analyze the performance, strengths, and limitations of these methodologies. Factors such as dataset size, computational resources, and application requirements are considered in the comparison. Our findings provide insights into the suitability of deep learning and the Haar cascade algorithm for various image identification tasks, aiding practitioners and researchers in selecting the most appropriate approach based on specific project needs. This research contributes to advancing the field of image processing and computer vision by offering a comprehensive analysis of these two prominent methodologies in digital image identification.