It appears you don't have support to open PDFs in this web browser. To view this file, Open with your PDF reader
Abstract
Aiming at the application scene of face image recognition in audit investigation, firstly, a face data set with corresponding environmental interference is simulated to increase the diversity of data; in the preprocessing stage, the improved method of adaptive histogram illumination balance and simulated glasses covering is used for image enhancement; in the model training stage, an optimal weight re overloading model training algorithm is proposed. The experimental results show that the accuracy, robustness and efficiency of face recognition in the application scene are improved by the improvement of image enhancement preprocessing and the model training of optimal weight reloading.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
Details
1 School of Computer Science & Engineering, Wuhan Institute of Technology, Wuhan, China; Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, China