Enhanced Face Detection Using Haar Cascade with Histogram Equalization, Sharpening, and Denoising for Real-Time Applications
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Abstract This study presents an enhanced face detection approach leveraging classical Haar Cascade classifiers combined with advanced image preprocessing techniques to improve detection accuracy and robustness. The proposed method applies a sequential pipeline of histogram equalization for contrast enhancement, sharpening filters to emphasize facial features, and non-local means denoising to reduce image noise. These preprocessing steps enhance the quality of the input images, enabling more reliable detection of faces under varying lighting and noise conditions. Experimental results demonstrate that integrating image enhancement techniques prior to Haar Cascade detection significantly reduces false negatives and improves the clarity of detected regions. This approach offers a computationally efficient alternative to deep learning methods for real-time face detection applications, particularly in environments with suboptimal image quality. The system is well-suited for live video or camera feeds, functioning effectively across different lighting and background conditions with minimal computational requirements, making it ideal for deployment in CCTV systems and mobile devices.
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