Small-scale face detection method based on generative adversarial network
The invention discloses a small-scale face detection method based on a generative adversarial network, relates to the technical field of face detection, and solves the technical problem of poor precision of an existing small-scale face detection mode. The method comprises the following steps: S1, in...
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creator | LIANG JIANPING SHAO QIQI YANG DASHEN TIAN ZHONGSHAN LAI SHAOCHUAN XIE CHENG SHAO QI WANG XIANZHONG |
description | The invention discloses a small-scale face detection method based on a generative adversarial network, relates to the technical field of face detection, and solves the technical problem of poor precision of an existing small-scale face detection mode. The method comprises the following steps: S1, inputting a noise image and a small face image in real acquisition data into a first generative adversarial network to generate enough small face context region samples; S2, training a face detection network by using the small face context region sample and the small face image; S3, acquiring a workplace image, and acquiring possible small-scale face candidate regions by using the potential face region network; and S4, performing super-resolution reconstruction on the small-scale face candidate region, and inputting the reconstructed small-scale face candidate region into the face detection network to obtain a small face position regression result. According to the method, sufficient small face context region samples |
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The method comprises the following steps: S1, inputting a noise image and a small face image in real acquisition data into a first generative adversarial network to generate enough small face context region samples; S2, training a face detection network by using the small face context region sample and the small face image; S3, acquiring a workplace image, and acquiring possible small-scale face candidate regions by using the potential face region network; and S4, performing super-resolution reconstruction on the small-scale face candidate region, and inputting the reconstructed small-scale face candidate region into the face detection network to obtain a small face position regression result. 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The method comprises the following steps: S1, inputting a noise image and a small face image in real acquisition data into a first generative adversarial network to generate enough small face context region samples; S2, training a face detection network by using the small face context region sample and the small face image; S3, acquiring a workplace image, and acquiring possible small-scale face candidate regions by using the potential face region network; and S4, performing super-resolution reconstruction on the small-scale face candidate region, and inputting the reconstructed small-scale face candidate region into the face detection network to obtain a small face position regression result. According to the method, sufficient small face context region samples</abstract><oa>free_for_read</oa></addata></record> |
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subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING HANDLING RECORD CARRIERS PHYSICS PRESENTATION OF DATA RECOGNITION OF DATA RECORD CARRIERS |
title | Small-scale face detection method based on generative adversarial network |
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