A novel bidirectional neural network for face recognition

2012 2nd International eConference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2012-10-01 DOI:10.1109/ICCKE.2012.6395345

Jalil Mazloum, A. Jalali, J. Amiryan

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引用次数: 6

Abstract

The recognition of face images is a complicated problem. Face images are often sufferedfrom variations in brightness, head rotation, facial emotions and so on. Besides, amazing abilities of human brain in face recognition in the presence of these variations, contribute to design face recognition systems based on procedure of human brain. Surveying the recognition and perceptual system of human, shows that, this system has hierarchical and bidirectional structure. Furthermore, the performance of the system would strongly be improved by applying the information of upper layers of face recognition system in interpreting and processing the input data. In this paper, novel bidirectional architecturefor face recognition inspired by human face recognition system is presentedvia applying inversion in artificial neural networks (ANN's). In this approach, storeddata in the inverse networkis applied in the recognition system iterativelyandthen the correctness of face recognition model has been consequently improved by 8%. The proposed model is able to produce 12 various facial expressions on the output, from only one input expressionof each person, after training with AUT database images.

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一种新的用于人脸识别的双向神经网络

人脸图像的识别是一个复杂的问题。人脸图像经常受到亮度、头部旋转、面部情绪等变化的影响。此外，在这些变化的情况下，人类大脑在人脸识别方面的惊人能力，有助于设计基于人类大脑过程的人脸识别系统。考察人类的识别和感知系统，发现该系统具有层次和双向结构。此外，利用人脸识别系统的上层信息对输入数据进行解释和处理，将大大提高系统的性能。本文在人脸识别系统的启发下，通过在人工神经网络(ANN’s)中应用反演，提出了一种新的双向人脸识别架构。该方法将存储在逆网络中的数据迭代应用到识别系统中，人脸识别模型的准确率提高了8%。在使用AUT数据库图像进行训练后，该模型能够从每个人的一个输入表情中产生12种不同的面部表情输出。

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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2012 2nd International eConference on Computer and Knowledge Engineering (ICCKE)

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