A Gender Classification Model Based on Cross-connected Convolutional Neural Networks
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摘要: 为提高性别分类准确率, 在传统卷积神经网络(Convolutional neural network, CNN)的基础上, 提出一个跨连卷积神经网络(Cross-connected CNN, CCNN)模型. 该模型是一个9层的网络结构, 包含输入层、6个由卷积层和池化层交错构成的隐含层、全连接层和输出层, 其中允许第2个池化层跨过两个层直接与全连接层相连接. 在10个人脸数据集上的性别分类实验结果表明, 跨连卷积网络的准确率均不低于传统卷积网络.Abstract: To improve gender classification accuracy, we propose a cross-connected convolutional neural network (CCNN) based on traditional convolutional neural networks (CNN). The proposed model is a 9-layer structure composed of an input layer, six hidden layers (i.e., three convolutional layers alternating with three pooling layers), a fully-connected layer and an output layer, where the second pooling layer is allowed to directly connect to the fully-connected layer across two layers. Experimental results in ten face datasets show that our model can achieve gender classification accuracies not lower than those of the convolutional neural networks.
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表 1 CCNN 的网络描述
Table 1 Description of the CCNN
Layer Type Patch size Stride Output size x Input 32×32 h1 Convolution 5×5 1 28×28×6 h2 Mean pooling 2×2 2 14×14×6 h3 Convolution 5×5 1 10×10×12 h4 Mean pooling 2×2 2 5×5×12 h5 Convolution 2×2 1 4×4×16 h6 Mean pooling 2×2 2 2×2×16 h7 Fully-connected 364 o Output 2 
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表 2 实验数据集的训练集和测试集信息描述
Table 2 Number of training samples and testing samples of the experiments
数据集 训练集 测试集 男 女 混合 男 女 混合 UMIST 209 57 266 95 19 114 ORL 320 30 350 40 10 50 Georgia Tech 450 75 525 195 30 225 FERET 658 532 1 190 105 105 Extended Yale B 1 280 384 1 664 576 192 768 AR 910 910 1 820 390 390 780 Faces94 2 000 400 2 400 660 20 680 LFW 8 000 1 900 9 900 2 000 800 2 800 MORPH 40 997 7 102 48 099 3 000 1 000 4 000 CelebFaces+ 27 887 37 113 65 000 2 500 2 500 5 000
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表 3 CNN 和CCNN 在10 个数据集上的分类准确率(%)
Table 3 Classi¯cation accuracies of CNN and CCNN in ten datasets (%)
数据集 CNN CCNN UMIST 96.49 99.20 ORL 98 98.00 Georgia Tech 97.6 97.78 FERET 94.77 96.44 Extended Yale B 98.53 98.82 AR 98.71 98.71 Faces94 96.46 97.35 LFW 87 87.86 MORPH 92.73 94.56 CelebFaces+ 85.18 88.70
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表 4 CNN 和CCNN 在4 个数据集上的分类准确率(%)
Table 4 Classi¯cation accuracies of CNN and CCNN in four datasets (%)
数据集 CNN CCNN 男 女 混合 男 女 混合 Georgia Tech 99.49 95.71 97.6 99.49 96.07 97.78 Extended Yale B 100 97.06 98.53 100 97.64 98.82 Faces94 98.48 94.44 96.46 100 94.7 97.35 LFW 95 79 87 96.8 78.92 87.86
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表 5 CCNN 在不同跨连方式的分类准确率(%)
Table 5 Classif cation accuracies of the CCNN withdifferent cross-connections (%)
数据集 h2-h7 h3-h7 h4-h7 h5-h7 Georgia Tech 97.96 97.84 97.78 97.33 AR 98.85 98.85 98.71 98.59 Faces94 97.5 97.35 97.35 97.35 LFW 88.13 88.04 87.86 87.86 MORPH 94.63 94.63 94.56 94.45
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