Optimization and OpenCV-Based Implementation of Face Detection Systems

Face detection, as a fundamental task in computer vision, holds significant value in applications such as security surveillance, human-computer interaction, and identity recognition. OpenCV, as an open-source computer vision library, provides various efficient face detection algorithms; however, it still faces challenges in complex scenarios, including insufficient detection accuracy and poor adaptability. This study systematically investigates the performance of traditional cascade classifiers (Haar and LBP) and deep learning models (DNN module) within the OpenCV framework, proposing a series of improvement methods.Firstly, through experimentation, we analyze the impact of detection parameters (e.g., scale factor, minimum neighbors) on performance and optimize the baseline detection pipeline. Secondly, to address challenges like illumination variations and pose diversity, we propose enhancement strategies based on image preprocessing (histogram equalization, noise suppression) and post-processing (optimized non-maximum suppression, false detection filtering). Furthermore, we explore a hybrid detection approach that combines cascade classifiers with deep learning models to improve robustness. Experimental results demonstrate that the improved method significantly enhances detection precision and recall rates on FDDB and WIDER FACE datasets, particularly showing better adaptability to low-light conditions, occlusions, and multi-angle faces. This research provides practical solutions for optimizing face detection in OpenCV environments, offering valuable references for related application development.