Face recognition evaluation of an association cortex - entorhinal cortex hippocampal formation model by successive learning

K. Nakamura, M. Yamazaki, Kakuen Ohzawa
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引用次数: 2

Abstract

Based on the visual information processing system in the brain, we developed an association cortex to entorhinal - hippocampal neural network model (AEH model) for learning and recollection of human faces. We evaluated the learning and recollection performance of the AEH model by computer simulation. We presented each human face to the AEH model twice. We regarded that the AEH model recognized each face correctly if the AEH model learned the face the first time and recalled it the second time, in accordance with human perception. In the experiment, ten human faces were successively presented to the AEH model. The recognition performance of the model was 100 percent correct, indicating the effectiveness of the representation and transformation algorithm of the AEH model.
连续学习评价联想皮层-内嗅皮层海马形成模型的人脸识别
基于大脑的视觉信息处理系统,我们建立了一个用于人脸学习和记忆的关联皮层-内嗅-海马神经网络模型(AEH模型)。通过计算机仿真对AEH模型的学习和记忆性能进行了评价。我们将每个人脸两次呈现给AEH模型。我们认为,如果AEH模型第一次学习人脸,第二次回忆人脸,符合人类感知,则AEH模型对每张人脸的识别是正确的。实验中,连续将10张人脸输入AEH模型。模型的识别准确率为100%,表明AEH模型的表示和转换算法是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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