针对复杂运动目标识别问题，提出了一个基于反馈型随机神经网络的运动认脸与物体的自动识别系统，该系统具有强大学习能力，运动目标检测与识别快速准确等特点，在对该的核心－反馈型二元网络进行深入分析的基础上，提出了一种适合于该神经网络模型的高效渐进式Boltzmann学习算法，实验结果表明，该识别系统性能优异，在几个方面超过了eTrue公司的TrueFace人脸识别系统。

This paper presents an automatic system for human face and moving object recognition. The system developed is based on a novel recurrent stochastic neural network, it has a strong learning power and is able to recognize a moving target in real time. The detection of the moving object is implemented by utilizing the skin color distribution and the motion information. The object is tracked in real time with an efficient adaptive mean shift algorithm. The work in this paper is mainly focused on the disign of the novel recurrent neural network and the efficient incremental Boltzmann learning algorithm. The improved simulated annealing technique is also discussed. Theoretical results offer a unique solution to the training of a large size network. Experiments on human face recognition are carried out with a recurrent neural network of 4827 neurons and 129951 connections. The results show the performance of the recognizer is comparable to that of the well known TrueFace system.