基于卷积神经网络的“画人”分类方法的实现

S. Widiyanto, Jhordy Wong Abuhasan
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引用次数: 1

摘要

心理测试或心理测试在人力资源的选择过程中经常被应用,目的是测量智力潜力,识别个性,预测工作表现,绘制潜力和生产力水平。长期以来,“画一个人”测试一直被用于衡量个性和了解个人的创作经历。该测试在心理测试中被心理学家机构广泛使用,因为测试的实施非常简单,只需要一支铅笔和一张纸。在实践中,心理学家需要相当长的时间来评估“画人”测试的结果。为了加快所需的时间并促进心理学家的工作,需要一个模型来识别和分类“画人”测试的结果。该模型能够识别和研究基于纸上人头大小图的绘图人测试结果。将深度学习与卷积神经网络相结合的方法应用于绘图人测试结果的识别与研究。为了提高CNN方法的可用性,数据采用数字图像的形式。使用智能手机相机收集数据,并根据图像上的标准在Microsoft Excel中逐一标记。已标记的数据将用于训练模型。将对训练好的模型进行新数据测试。在本研究中,数据训练达到了99.48%的准确率和1.74%的损失。在新数据中,该模型的准确率达到了66.7%
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implementation The Convolutional Neural Network Method For Classification The Draw-A-Person Test
Psychotest or psychological tests at this time are often applied in the process of selection of human resources aimed at measuring the potential of intelligence, recognizing personality, predicting work performance, mapping potential, and level of productivity. The Draw-A-Person test has been long applied to measure personality and to know the individual's creative experience. This test is widely used by psychologist institution in Psychotest because the implementation of test is quite simple that only use a pencil as well as paper. In practice, a psychologist takes quite a long time to assess the result of the Draw-A-Person test. To accelerated the required time and facilitate the work of a psychologist, a model is needed to recognize and classify the results of a Draw-A-Person test. This model is able to recognize and study the Draw-A-Person test result based on the head-size drawings on paper. Deep learning with a convolutional neural network method is applied to recognize and study the Draw-A-Person test result. To improve the usability of CNN method, the data is in the form of a digital image. The data is collected using a smartphone camera and labeled in Microsoft Excel one by one according to the criteria on the image. Data that has been labeled will be used to train the model. The trained model will be tested for new data. In this research, the data train achieves 99.48% accuracy and 1.74% loss. In the new data, the model achieved 66.7% accuracy
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