Face Detection Based on DF-Net

Face data have found increasingly widespread applications in daily life. To efficiently and accurately extract face information from input images, this paper presents a DF-Net-based detection approach. A lightweight facial feature extraction neural network based on the MobileNet-v2 architecture is designed implemented. By incorporating multi-scale fusion spatial pyramid modules, system achieves localization across multiple scales. The proposed trained open-source dataset WiderFace. hyperparameters such as bottleneck coefficients quality factors are discussed. Comparative experiments with other commonly used networks carried out terms of model size, processing speed, accuracy. Experimental results affirm efficacy robustness method, especially challenging poses.