8 December 2015 Adaptive WildNet Face network for detecting face in the wild
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Proceedings Volume 9875, Eighth International Conference on Machine Vision (ICMV 2015); 98750S (2015) https://doi.org/10.1117/12.2229889
Event: Eighth International Conference on Machine Vision, 2015, Barcelona, Spain
Abstract
Combining Convolutional Neural Network and Deformable Part Models is a new trend in object detection area. Following this trend, we propose Adaptive WildNet Face network using Deformable Part Models structure to exploit advantages of two methods in face detection area. We evaluate the merit of our method on Face Detection Data Set and Benchmark. Experimental results show that our method achieves up to 86.22% true positive images in 1000 false positive images in FDDB. Our method becomes one of state-of-the-art methods in FDDB dataset and it opens a new way to detect faces of images in the wild.
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Dinh-Luan Nguyen, Dinh-Luan Nguyen, Vinh-Tiep Nguyen, Vinh-Tiep Nguyen, Minh-Triet Tran, Minh-Triet Tran, Atsuo Yoshitaka, Atsuo Yoshitaka, } "Adaptive WildNet Face network for detecting face in the wild", Proc. SPIE 9875, Eighth International Conference on Machine Vision (ICMV 2015), 98750S (8 December 2015); doi: 10.1117/12.2229889; https://doi.org/10.1117/12.2229889
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