Journal of South China University of Technology (Natural Science Edition) ›› 2020, Vol. 48 ›› Issue (6): 106-113,133.doi: 10.12141/j.issn.1000-565X.190860

• Computer Science & Technology • Previous Articles     Next Articles

FtH-Net Method for Predicting Height Based on Footprint Image

WANG Nian1 FAN Xuchen1 ZHANG Yuming1 LU Xilong2 CHEN Feng1   

  1. 1. School of Electronics and Information Engineering,Anhui University,Hefei 230601,Anhui,China;2. Institute of Forensic Science,Ministry of Public Security,Beijing 100038,China
  • Received:2019-11-25 Revised:2020-01-26 Online:2020-06-25 Published:2020-06-01
  • Contact: 王年(1966-),男,博士,教授,主要从事计算机视觉与模式识别研究。 E-mail:wn_xlb@ahu.edu.cn
  • About author:王年(1966-),男,博士,教授,主要从事计算机视觉与模式识别研究。
  • Supported by:
    Supported by the Key Research and Development Program of China (2018YFC0807302) and the National Na-tural Science Foundation of China (61772032)

Abstract: A regression prediction algorithm based on deep learning was proposed to solve the problem of predicting height through footprint information in criminal investigation. Firstly,the original data was preprocessed to obtain the data set suitable for the deep learning regression model. Secondly,a new network architecture foot to height-net (FtH-Net) containing edge extraction and regression was proposed according to the characteristics of the footprint data. Finally,a prediction model with good performance was achieved by the data set based on the first two steps and regression network training. The experimental results show that,compared with the traditional one,the new method can greatly improve the accuracy of prediction while ensuring the generalization ability of the model,and the accuracy of prediction for people within 2cm height can reach 67%.

Key words: criminal investigation, deep learning, footprint image, height prediction, regression model