A Hybrid Model of Face Detection and Motion Tracker System(FDMT) using Bacterial Foraging Optimization and Colony Approximation: A Comparative Study

Motion tracking and face detection are essential for many applications, such as augmented reality, biometric authentication, surveillance, and human-computer interaction. Conventional techniques like convolutional neural networks (CNNs), Haar cascades, and histogram of oriented gradients (HOG) have made great strides in face detection, but they frequently struggle with real-time tracking, occlusion handling, and processing efficiency. This study compares a hybrid Face Detection and Motion Tracking (FDMT) model that incorporates Ant Colony Approximation (ACA) and Bacterial Foraging Optimization (BFO) with traditional methods in order to get over these drawbacks. BFO is an evolutionary algorithm used for face detection feature selection and parameter tweaking that draws inspiration from the foraging behavior of bacteria. Inspired on ants' foraging habits, ACA is used to forecast motion trajectories and improve tracking robustness. Using datasets including FDDB (Face Detection Database), LFW (Labeled Faces in the Wild), and bespoke real-time video sequences, this comparative study assesses the suggested FDMT model against traditional CNN-based and optical flow-based approaches. According to experimental results, the FDMT model performs better than conventional techniques in terms of tracking precision (92.8% vs. 87.6% for optical flow), detection accuracy (97.2% vs. 90.4% for Haar and 95.1% for CNN-based models), and computational efficiency (28% improvement in processing time). The paper offers a thorough comparison of optimization-based strategies with traditional techniques, emphasizing the trade-offs between computing complexity, performance, and robustness. According to the results, hybrid bio-inspired algorithms may greatly improve real-time motion tracking and face detection applications, which makes them appropriate for use in security systems, autonomous navigation, and intelligent surveillance. Accuracy is enhanced by using ensemble learning techniques and reducing noise in pre-processing.