基于GNSA多尺度模型的人脸识别

考虑近似图像信息和细节图像信息,提出了 GNSA 多尺度模型。该模型利用具有 1 个隐含层和 50 个隐单元的神经网络建立不同尺度图像间的映射关系;使用反向传播算法训练神经网络,确定这种映射关系;根据该映射关系由低分辨力图像估计高分辨力图像。采用亮度相似性和对比度相似性量化估计图像与目标图像间的相似程度。实验表明,以该模型分析得到的两种相似性分别为 89.907%和 96.196%;以该模型为基础的人脸识别系统对光照具有很好的鲁棒性。

Face recognition based on GNSA multi-scale model

A multi-scale model named GNSA is proposed under the condition of taking account of approximate image information and detailed image information. The mapping relationship among different scale images is established by using a neural network with a hidden layer and 50 hidden units. This mapping relation is determined by training neural network with a back-propagation algorithm, which is utilized to estimate images at finer resolution from coarser versions. Similarity in brightness and similarity in contrast are used to gauge the degree of similarity between the estimated images and target images. The experiments show that two similarities between images estimated by GNSA model and target images are 89.907% and 96.196% respectively. The face recognition system based on the model has a good robustness for light illumination.