激光与光电子学进展, 2020, 57 (16): 161005, 网络出版: 2020-08-05
融合梯度特征的轻量级神经网络的人脸识别 下载: 764次
Face Recognition Based on Lightweight Neural Network Combining Gradient Features
图像处理 嵌入式设备 轻量级卷积神经网络 一阶梯度特征 人脸识别 image processing embedded device lightweight convolutional neural network first-step gradient feature face recognition
摘要
深度学习在人脸识别的研究和应用中取得一定成效,但因计算量大且耗时,不适用于小型嵌入式设备。基于融合梯度特征的轻量级卷积神经网络SqueezeNet提取人脸特征,既能保证该网络模型适用于内存相对小的嵌入式设备,又能保证获得的人脸特征对不同光照更具鲁棒性。实验结果表明,将8×8分块图像中提取的一阶梯度特征,与轻量级卷积神经网络提取的全局特征相融合的人脸识别算法,在LFW数据集中识别率可达97.28%,较传统轻量级卷积神经网络的人脸识别方法,识别率提高了4.36%。
Abstract
Deep learning has impacted the research and application of face recognition to some extent; however, it is unsuitable for small embedded devices owing to its large computational cost and time consumption. Herein, a facial feature extraction method for integrating gradient features in a lightweight convolutional neural network (SqueezeNet) was proposed to ensure the application of the network model to embedded devices with relatively small memory and facial features that are more robust to different lightings. Experimental results showed that the lightweight convolutional neural network integrating the first-step gradient feature extracted by dividing the image into a block of 8 × 8 can achieve a recognition rate of up to 97.28% in LFW dataset, which is 4.36% higher than that of the conventional lightweight convolutional neural network.
刘祥楼, 李天昊, 张明. 融合梯度特征的轻量级神经网络的人脸识别[J]. 激光与光电子学进展, 2020, 57(16): 161005. Xianglou Liu, Tianhao Li, Ming Zhang. Face Recognition Based on Lightweight Neural Network Combining Gradient Features[J]. Laser & Optoelectronics Progress, 2020, 57(16): 161005.