A NEW METHOD FOR FACE RECOGNITION USING CONVOLUTIONAL NEURAL NETWORK
A NEW METHOD FOR FACE RECOGNITION USING CONVOLUTIONAL NEURAL NETWORK
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DOI：
10.15598/aeee.v15i4.2389
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发表时间：
2017-01-01
影响因子：
0.6
通讯作者：
Radil, Roman
中科院分区：
文献类型：
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作者：
Kamencay, Patrik;Benco, Miroslav;Radil, Roman
本文采用主成分分析（PCA）、局部二值模式直方图（LBPH）和k近邻（KNN）三种著名的图像识别方法，对所提出的卷积神经网络（CNN）的性能进行了测试。在我们的实验中，验证了PCA、LBPH、KNN和提出的CNN的整体识别精度。所有实验都在ORL数据库上进行，并对得到的实验结果进行了展示和评价。该人脸数据库由400个不同的受试者组成（40个类别/每个类别10张图像）。实验结果表明，LBPH比PCA和KNN具有更好的效果。在ORL数据库上的实验结果证明了该方法在人脸识别中的有效性。对于所提出的CNN，我们获得了98.3%的最佳识别准确率。提出的基于CNN的方法优于当前的方法。
In this paper, the performance of the proposed Convolutional Neural Network (CNN) with three well-known image recognition methods such as Principal Component Analysis (PCA), Local Binary Patterns Histograms (LBPH) and K-Nearest Neighbour (KNN) is tested. In our experiments, the overall recognition accuracy of the PCA, LBPH, KNN and proposed CNN is demonstrated. All the experiments were implemented on the ORL database and the obtained experimental results were shown and evaluated. This face database consists of 400 different subjects (40 classes/10 images for each class). The experimental result shows that the LBPH provide better results than PCA and KNN. These experimental results on the ORL database demonstrated the effectiveness of the proposed method for face recognition. For proposed CNN we have obtained a best recognition accuracy of 98.3 %. The proposed method based on CNN outperforms the state of the art methods.