Vision is an important way for human beings to interact with the outside world and obtain information. In order to research human visual behavior under different conditions, this paper uses a Gaussian mixture-hidden Markov model (GMM-HMM) to model the scanpath, and proposes a new model optimization method, time-shifting segmentation (TSS). The TSS method can highlight the characteristics of the time dimension in the scanpath, improve the pattern recognition results, and enhance the stability of the model. In this paper, a linear discriminant analysis (LDA) method is used for multi-dimensional feature pattern recognition to evaluates the rationality and the accuracy of the proposed model. Four sets of comparative trials were carried out for the model evaluation. The first group applied the GMM-HMM to model the scanpath, and the average accuracy of the classification could reach 0.507, which is greater than the opportunity probability of three classification (0.333). The second set of trial applied TSS method, and the mean accuracy of classification was raised to 0.610. The third group combined GMM-HMM with TSS method, and the mean accuracy of classification reached 0.602, which was more stable than the second model. Finally, comparing the model analysis results with the saccade amplitude (SA) characteristics analysis results, the modeling analysis method is much better than the basic information analysis method. Via analyzing the characteristics of three types of tasks, the results show that the free viewing task have higher specificity value and a higher sensitivity to the cued object search task. In summary, the application of GMM-HMM model has a good performance in scanpath pattern recognition, and the introduction of TSS method can enhance the difference of scanpath characteristics. Especially for the recognition of the scanpath of search-type tasks, the model has better advantages. And it also provides a new solution for a single state eye movement sequence.
视觉是人类与外界交互并获取信息的重要方式。为了研究在不同条件下人类的视觉行为，本文采用了混合高斯-隐马尔可夫模型（GMM-HMM）对扫视过程中的眼动路径进行建模，并提出了一种新的模型优化方法——时移分段法（TSS）。TSS 方法可突出眼动序列中时间维度的特征，提升模式识别结果，增强模型稳定性。本研究对多维特征模式识别采用了线性判别分析（LDA）方法，以评价各模型的合理性及识别的准确性。全文共进行了四组对比试验，第一组应用了 GMM-HMM 模型对眼动路径进行建模分类，三分类准确率均值可达到 0.507，大于三分类机会概率（0.333）；第二组试验应用 TSS 方法，分类准确率均值提高至 0.610；第三组将 GMM-HMM 与 TSS 结合，分类准确率均值达到 0.602，且相较于第二组模型更稳定；最后，将模型分析结果与眼跳（SA）等特征分析结果进行比较，建模分析方法远好于基础信息分析方法。同时，通过对三类任务特性分析，结果显示，自由查看任务特异性较高，而对象搜寻任务的敏感度较高。综上所述，GMM-HMM 模型应用在眼动模式识别领域有较好的特征提取效果，引入 TSS 方法可以加强眼动特征差异，尤其对搜寻类任务的眼动路径识别有更好的优势，也为单一状态眼动序列提供了新的解决方案。.
Keywords: Gaussian mixture-hidden Markov model; pattern recognition; scanpath; time-shifting segmentation method; visual behavior.