Abstract
This paper presents a novel technique to extract facial Eye Like Landmarks (ELL) for face detection. The proposed technique pre-processes input images for skin segmentation using a minimal approach which segments all the skin regions in a fragmented manner. Morphological closing further enhances fragmented skin regions. Contrast Limited Adaptive Histogram Equalization (CLAHE) is used to enhance eye like regions. The ELL are extracted from skin regions By using divide and conquer method with a heuristically calculated threshold. While Extracting ELL the rotation and scale are taken into consideration. The pair of ELL which satisfy criteria to be a possible eye pair is then combined to form a patched image which is smaller in dimension, hence easier and faster to process. Patched images are used to train a Deep Neural Network. The classification accuracy on the face and non-face patches is 97%. In the result section, we discuss the training and validation loss. The Extracted Patches can be further processed to extract features for robust face detection.
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Acknowledgements
Authors thank the Ministry of Electronics and Information Technology (MeitY), New Delhi for granting Visvesvaraya Ph.D. fellowship through file no. PhD-MLA\(\backslash \)4(34)\(\backslash \)2014-15 Dated: 10/04/2015.
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Sawat, D.D., Hegadi, R.S., Hegadi, R.S. (2019). Eye Like Landmarks Extraction and Patching for Face Detection Using Deep Neural Network. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1036. Springer, Singapore. https://doi.org/10.1007/978-981-13-9184-2_36
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