A Deep Learning-Based Framework for Robust Facial Keypoint Localization in Unconstrained Conditions

Facial keypoint localization plays a critical role in facial recognition, security monitoring, and human-computer interaction.Traditional methods rely heavily on handcrafted features, making them sensitive to occlusions, lighting variations, and pose changes.This study proposes a deep learning-based framework integrating lightweight convolutional neural networks (CNNs) and Conditional Random Fields (CRFs) to improve keypoint detection and localization accuracy under unconstrained conditions.A fast connected convolutional layer is introduced in a cascaded network structure, significantly reducing feature space information loss and enhancing geometric relationship modeling.The results showed that the proposed face detection model had a small cumulative error value, with a feature recognition accuracy of over 0.9, and an average accuracy of over 90 for all classes under three different image conditions.The proposed localization model had smaller error values and a much lower error rate than other algorithms under various segmented image differences, effectively considering data feature differences and achieving higher localization accuracy.The proposed deep learning model can effectively achieve the fusion of output features and improve the effectiveness of facial keypoint localization.