Background and Objective

Face recognition success rate is influenced by illumination, expression, posture change, and other factors, which is due to the low generalization ability of a single convolutional neural network. A new face recognition method based on parallel ensemble learning of convolutional neural networks (CNN) and local binary patterns (LBP) is proposed to solve this problem. It also helps to improve the low pedestrian detection rate caused by occlusion.

Methods

First, the LBP operator is employed to extract features of the face texture. After that, 10 convolutional neural networks with 5 different network structures are adopted to further extract features for training, to improve the network parameters and get classification result by using the Softmax function after the layer is fully connected. Finally, the method of parallel ensemble learning is used to generate the final result of face recognition using majority voting.

Results

By this method, the recognition rates in the ORL and Yale-B face datasets increase to 100% and 97.51%, respectively. In the experiments, the proposed approach is illustrated not only enhances its tolerance to illumination, expression, and posture but also improves the accuracy of face recognition and the poor generalization performance of the model, which is normally caused by the learning algorithm being trapped in a local minimum. Moreover, the proposed method is combined with a pedestrian detection model as a hybrid model for improving the detection rate, which shows in the result that the detection rate is improved by 11.2%.

Conclusion

In summary, the proposed approach greatly outperforms other competitive methods.

中文翻译：

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卷积神经网络和局部二进制模式的人脸识别并行集成学习。

背景与目的

人脸识别成功率受光照，表情，姿势变化和其他因素的影响，这是由于单个卷积神经网络的泛化能力低而导致的。为解决这一问题，提出了一种基于卷积神经网络（CNN）和局部二进制模式（LBP）的并行集成学习的人脸识别新方法。这也有助于改善由阻塞引起的低行人检测率。

方法

首先，使用LBP运算符提取面部纹理的特征。此后，采用10个具有5种不同网络结构的卷积神经网络进一步提取训练特征，以改善网络参数并在完全连接层之后使用Softmax函数获得分类结果。最后，采用并行集成学习的方法通过多数投票生成人脸识别的最终结果。

结果

通过这种方法，ORL和Yale-B人脸数据集中的识别率分别提高到100％和97.51％。在实验中，说明了该方法不仅增强了其对光照，表情和姿势的容忍度，而且还提高了人脸识别的准确性和模型的泛化性能差，这通常是由于学习算法被困在一个局部最小值。此外，提出的方法结合行人检测模型作为混合模型以提高检测率，结果表明检测率提高了11.2％。

结论

总之，所提出的方法大大优于其他竞争方法。