DEEPFACS: A Deep Learning Framework for Facial Component Segmentation using a BFO SBO Hybrid Feature-Selection Strategy

A deep learning model of segmentation and analysis of facial components combining efficient preprocessing, hybrid segmentation, multi-feature extraction, and cooperative feature selection. The preprocessing step uses Contrast-Limited Adaptive Histogram Equalization (CLAHE), Tensorial Adaptive Anisotropic Diffusion (TAAD) and Active Appearance Modeling (AAM) to improve the image clarity and structural integrity. The segmentation is done with a SqueezeNetAttention U-Net hybrid architecture that combines the performance lightweight of SqueezeNet with space attention of Attention U-Net to ensure that the component is properly delineated. Multi-source feature extraction combines Local Phase Congruency (LPC), Histogram of Oriented Gradients (HOG), Color Histograms, Gradient Orientation Tensor (GOT) and ResNet embeddings to extract a variety of facial signals. A hybrid feature-selection method, termed Hybrid BFO-SBO Feature Selection (HBFS), is proposed, which consecutively employs Bacterial Foraging Optimization (BFO) for global exploration and Secretary-Bird Optimization (SBO) for local exploitation. Finally, facial component detection is performed using the proposed DEEPFACS framework, in which a ResAttNet-LSTM network jointly integrates a ResNet backbone, self-attention blocks, and LSTM units to effectively learn spatial relationships and achieve accurate classification. The experimental assessments on CelebAMask-HQ and Helen datasets show that the segmentation accuracy (98.7%) and robustness are better than the baseline feature-selection methods. The framework has a low inference latency and used in real-time facial component analysis and human computer interaction.