Abstract: Due to lack of depth information in 2D pictures, it is difficult to obtain the three- dimensional size information of the human body. The size information fitted by the classical linear regression method is the mean value of the threshold interval to which the human body belongs. Because the heterogeneity of the human body is ignored, the size error of the fitting is very large. Model reconstruction can improve the accuracy of size acquisition. However, due to the large scale of computations and parameters in deep neural network, it is difficult to deploy it in mobile devices. Therefore, a 3D human dimension prediction model based on improved GA-BP-MC neural network is proposed. This model optimizes the BP network structure, weights and thresholds by upgrading the adaptive crossover and mutation probability of genetic algorithm. In addition, the Markov residual network is used to improve the prediction accuracy of the UGA-BP model. Finally, data comparison and analysis of 210 sets of samples were carried out through engineering examples, and the results show that, compared with Hyperelliptic curve method, multivariate function and GA-BP model, the proposed UGA-BP-MC reduces the average prediction error by 2.8cm, 1.62cm and 0.94cm respectively.

Key words: genetic algorithm, BP neural network, Markov chain, dimension prediction, non-contact measurement