International Journal on Science and Technology

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MSAP-Net: A Hierarchical Multi-Scale Adaptive Preprocessing Framework for Robust Face Recognition under Occlusion and Pose Variations

Author(s) Mr. Samadhan S. Ghodke, Prof. Dr. Prapti D. Deshmukh, Ms. Shalini R. Bakal, Ms. Manisha B. More
Country India
Abstract Face recognition in unconstrained environments remains challenging due to occlusion, pose variations, illumination changes, and unreliable face alignment. This paper presents MSAP-Net, a hierarchical multi-scale adaptive preprocessing framework designed to enhance face recognition robustness under such conditions. The proposed method integrates color space normalization, adaptive face detection with intelligent upsampling, context-aware padding, landmark confidence estimation, and confidence-weighted face alignment prior to deep feature extraction. Unlike fixed preprocessing pipelines, MSAP-Net applies selective and adaptive preprocessing to preserve discriminative facial features and avoid feature degradation. Experimental evaluation on unconstrained face datasets demonstrates that refining landmark detection and preprocessing significantly improves verification performance, achieving a 7% increase in accuracy and a 10% improvement in AUC, with a corresponding reduction in equal error rate. The results confirm that adaptive preprocessing and reliable alignment play a crucial role in improving recognition robustness, particularly for face verification tasks. While identification performance remains limited due to feature discriminability constraints, MSAP-Net provides a practical and extensible foundation for robust, edge-deployable face recognition systems.
Keywords Face Recognition, Hybrid Preprocessing, Deep Learning, Unconstrained Environments; Occlusion Handling; Pose Variation; Adaptive Preprocessing; Landmark-Based Alignment; Face Verification; Edge Computing
Field Computer Applications
Published In Volume 16, Issue 4, October-December 2025
Published On 2025-12-29
DOI https://doi.org/10.71097/IJSAT.v16.i4.10026
Short DOI https://doi.org/hbhj5x
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