卷积神经网络算法在近红外光人脸检测中的研究
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摘要
针对当前卷积神经网络算法在近红外光人脸检测时,普遍存在着检测时间过长、检测性能较低等问题。提出一种基于粒子优化群算法和卷积神经网络的人脸检测方法。通过对近红外光人脸进行分析,在人脸图像预处理中对人脸进行近红外光光照强度补偿,利用小波变换方法提取人脸图像低频子带,引入主特征分析法提取出近红外光人脸图像特征分量,将神经网络参数进行初始化,确定其拓扑结构。将提取的特征向量输入神经网络中,引入粒子群优化算法对神经网络进行优化,完成卷积神经网络算法在近红光中的人脸检测。实验结果表明,本文方法在近红外光人脸检测中检测时间较短,检测性能较高。
In view of the current convolutional neural network algorithm in the near-infrared face detection,there are generally problems such as long detection time and low detection performance. A face detection method based on particle optimization group algorithm is proposed. By analyzing the face of near-infrared light,the face infrared image is compensated for the near-infrared light intensity in the face image preprocessing. The wavelet transform method is used to extract the low-frequency sub-band of the face image,and the main feature analysis method is introduced to extract the near-infrared. The light facial image feature component initializes the neural network parameters and determines its topology. The extracted feature vector is input into the neural network,and the particle swarm optimization algorithm is introduced to optimize the neural network,and the face detection of the convolutional neural network algorithm in near red light is completed. The experimental results show that the proposed method has shorter detection time and higher detection performance in near-infrared face detection.
In view of the current convolutional neural network algorithm in the near-infrared face detection,there are generally problems such as long detection time and low detection performance. A face detection method based on particle optimization group algorithm is proposed. By analyzing the face of near-infrared light,the face infrared image is compensated for the near-infrared light intensity in the face image preprocessing. The wavelet transform method is used to extract the low-frequency sub-band of the face image,and the main feature analysis method is introduced to extract the near-infrared. The light facial image feature component initializes the neural network parameters and determines its topology. The extracted feature vector is input into the neural network,and the particle swarm optimization algorithm is introduced to optimize the neural network,and the face detection of the convolutional neural network algorithm in near red light is completed. The experimental results show that the proposed method has shorter detection time and higher detection performance in near-infrared face detection.
引文
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[4]孔英会,王之涵,车辚辚.基于卷积神经网络(CNN)和CUDA加速的实时视频人脸识别[J].科学技术与工程,2016,16(35):96-100.
[5]史鹤欢,许悦雷,马时平,等.PCA预训练的卷积神经网络目标识别算法[J].西安电子科技大学学报(自然科学版),2016,43(3):161-166.
[6]杨燕,樊林庆.基于光照变换的Gabor小波人脸识别[J].计算机工程与应用,2016,52(5):220-224.
[7]韩袁琛,程勇.基于朗伯反射模型的光照估计及鲁棒人脸识别[J].电视技术,2017,41(1):79-83.
[8]赵亮,王晓峰,袁逸涛.基于深度卷积神经网络的船舶识别方法研究[J].舰船科学技术,2016,38(15):119-123.
[9]刘万军,梁雪剑,曲海成.基于双重优化的卷积神经网络图像识别算法[J].模式识别与人工智能,2016,29(9):856-864.
[10]杜兰,刘彬,王燕,等.基于卷积神经网络的SAR图像目标检测算法[J].电子与信息学报,2016,38(12):3018-3025.
[11]刘长征,张磊.语音识别中卷积神经网络优化算法[J].哈尔滨理工大学学报,2016,21(3):34-38.
[12]卢伟,胡海阳,王家鹏,等.基于卷积神经网络面部图像识别的拖拉机驾驶员疲劳检测[J].农业工程学报,2018,34(7):192-199.
[13]刘万军,梁雪剑,曲海成.自适应增强卷积神经网络图像识别[J].中国图象图形学报,2017,22(12):1723-1736.
[14]吴尧,邱卫根.基于改进的深度卷积神经网络的人脸识别[J].计算机工程与设计,2017,38(8):2246-2250.
[15]李亚,王广润,王青.基于深度卷积神经网络的跨年龄人脸识别[J].北京邮电大学学报,2017,40(1):84-88.