Abstract
Image processing focuses on improving image color quality. According to our research, low-pass and high-pass filters boost color images for face detection. We also chosen 621,107 pictures of 936 people, scaled them to 160 × 160 pixels, and modified the color format from RGB to YUV or BGR. The composition of BRG, YUV, and RGB with a low-pass or high-pass filter improves image quality. This study proposes six approaches, the best of which yields 95.12% accuracy compared to the original’s 94.15%. Next, measure accuracy using YouTube Faces (YTF) and Dlib’s face detector. In addition, the peak signal-to-noise ratio (PSNR) and the mean squared error (MSE) are used to measure image noise.
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Acknowledgement
The research work is mainly contributed by the first author’s during the studies of Master in Computer Sciences programme, University of Wollongong Malaysia KDU Penang University College, supervised by the second author, Dr. Khoo Hee Kooi and co-supervised by Dr. J. Joshua Thomas.
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Son, B.H., Khoo, H.K., Thomas, J.J. (2023). Spatial Features Enhancement on Facial Landmarks for Face Detection. In: Vasant, P., Weber, GW., Marmolejo-Saucedo, J.A., Munapo, E., Thomas, J.J. (eds) Intelligent Computing & Optimization. ICO 2022. Lecture Notes in Networks and Systems, vol 569. Springer, Cham. https://doi.org/10.1007/978-3-031-19958-5_84
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