Person Recognition based on Gait Model (PRGM) and motion features is are indeed a challenging and novel task due to their usages and to the critical issues of human pose variation, human body occlusion, camera view variation, etc. In this project, a deep convolution neural network (CNN) was modified and adapted for person recognition with Image Augmentation (IA) technique depending on gait features. Adaptation aims to get best values for CNN parameters to get best CNN model. In Addition to the CNN parameters Adaptation, the design of CNN model itself was adapted to get best model structure; Adaptation in the design was affected the type, the number of layers in CNN and normalization between them. After choosing best parameters and best design, Image augmentation was used to increase the size of train dataset with many copies of the image to boost the number of different images that will be used to train Deep learning algorithms. The tests were achieved using known dataset (Market dataset). The dataset contains sequential pictures of people in different gait status. The image in CNN model as matrix is extracted to many images or matrices by the convolution, so dataset size may be bigger by hundred times to make the problem a big data issue. In this project, results show that adaptation has improved the accuracy of person recognition using gait model comparing to model without adaptation. In addition, dataset contains images of person carrying things. IA technique improved the model to be robust to some variations such as image dimensions (quality and resolution), rotations and carried things by persons. Results for 200 persons recognition, validation accuracy was about 82% without IA and 96.23 with IA. For 800 persons recognition, validation accuracy was 93.62% without IA.

基于步态模型（PRGM）和运动特征的人物识别确实是一项具有挑战性和新颖性的任务，因为它们的用途以及人体姿势变化、人体遮挡、摄像机视图变化等关键问题。在这个项目中，深入研究了卷积神经网络（CNN）经过修改并适用于根据步态特征使用图像增强（IA）技术进行人物识别。适应的目的是获得 CNN 参数的最佳值，以获得最佳的 CNN 模型。除了CNN参数适配外，还对CNN模型本身的设计进行了适配，以获得最佳的模型结构；设计中的适应性会影响 CNN 的类型、层数以及它们之间的归一化。选择最佳参数和最佳设计后，图像增强用于增加训练数据集的大小，其中包含许多图像副本，以增加用于训练深度学习算法的不同图像的数量。测试是使用已知数据集（市场数据集）完成的。该数据集包含处于不同步态状态的人的连续图片。 CNN模型中的图像作为矩阵，通过卷积被提取为许多图像或矩阵，因此数据集大小可能会大数百倍，使问题成为大数据问题。在这个项目中，结果表明，与没有自适应的模型相比，自适应提高了使用步态模型进行人物识别的准确性。此外，数据集包含携带物品的人的图像。 IA 技术改进了模型，使其对某些变化具有鲁棒性，例如图像尺寸（质量和分辨率）、旋转和人员携带的物品。 200 人的识别结果，验证准确度在没有 IA 的情况下约为 82%，有 IA 的情况下为 96.23。对于 800 人的识别，验证准确度为 93。62% 没有 IA。