Abstract:

ABSTRACT-Enhanced face recognition systems have gained significant attention in recent years due to their broad applications in security, surveillance, human-computer interaction, and smart devices. Traditional face detection techniques often struggle with challenges such as variations in lighting conditions, occlusions, or the presence of noise in visual data. To address these challenges, this paper proposes an advanced approach for enhanced face detection using multi-modal data fusion. By integrating multiple sensor modalities—such as RGB image data, depth information, infrared (IR) data, and optionally, audio signals—our approach leverages the complementary strengths of each modality to improve detection accuracy and robustness. The proposed framework employs sophisticated preprocessing techniques to enhance each data stream, followed by multi-modal feature extraction that captures texture, depth, and thermal cues. Data fusion strategies, including early fusion (feature-level), late fusion (decision-level), and hierarchical fusion, are applied to combine information from different sources in an optimal manner. A deep convolutional neural network (CNN) architecture is utilized for face detection, with the fusion of features from various modalities improving the model's ability to detect faces under varying environmental conditions. Post-processing techniques, such as non-maximum suppression and temporal smoothing, further refine the results. The proposed system demonstrates significant improvements in detection accuracy, especially under challenging conditions such as low light, partial occlusion, or 3D face recognition tasks. Experimental results show that the multi-modal data fusion approach outperforms traditional single-modality face detection systems, making it a promising solution for real-time and high-precision face detection applications.

Description:

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