Facedetectnet: face detection via fully-convolutional network

Facedetectnet: face detection via fully-convolutional network

Автор: Gorbatsevich Vladimir Sergeevich, Moiseenko Anastasia Sergeevna, Vizilter Yury Valentinovich

Журнал: Компьютерная оптика @computer-optics

Рубрика: Обработка изображений, распознавание образов

Статья в выпуске: 1 т.43, 2019 года.

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Ace detection is one of the most popular computer vision tasks. There are a lot of face detection approaches proposed including different CNN-based techniques, but the problem of optimal balancing between detection quality and computational speed is still relevant. In this paper we propose new CNN-based solution for face detection called FaceDetectNet. Our CNN architecture is based on ideas of YOLO/DetectNet and GoogleNet architecture supported with some new tools and implementation details created especially for our face detection application. We propose: original iterative proposal clustering (IPC) algorithm for aggregation of output face proposals formed by CNN and the 2-level “weak pyramid” providing better detection quality on the testing sets containing both small and huge images. Our face detection approach is close to previously proposed SSD-based face detection, but the principal difference is that we use the deep features of top hidden CNN layer for forming the face proposals of any size...

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Cnn, face detection, detectnet, yolo

Короткий адрес: https://sciup.org/140243269

IDR: 140243269   |   DOI: 10.18287/2412-6179-2019-43-1-63-71

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