Face Recognition and Age Estimation Based on Varying Illumination

Aiming at the problem of environmental illumination in face recognition and age estimation system, a face recognition and age estimation method based on multi-task convolutional neural network under varying illumination is proposed. The recognition rate of face images and the accuracy of age estimation under varying illumination are improved by the proposed method. Retinex image enhancement algorithm in YCbCr color space is used to improve the accuracy of face recognition and age estimation, and the face recognition and age estimation experiments under 10 kinds of dimming level for three kinds of distance are carried out. Experimental results show that compared with the original images, the recognition rates of the face images obtained by the improved method are improved, and the average absolute errors of age estimation are decreased. When the dimming level is 40%, and the distance is 1, 2, and 3 m, the face recognition rates are increased by 3 percentage points, 19 percentage points, and 25 percentage points, and the average absolute error of age estimation is decreased by 1.20, 2.99 and 2.00. At the same time, it is found that the effect of face recognition and age estimation is better when the gray mean value of face image without image enhancement algorithm is more than 50.18. When it is lower than the value, it is necessary to add the image enhancement algorithm to improve the accuracy of face recognition and age estimation. After adding the image enhancement algorithm, when the gray mean value of the face image is more than 56.61, the effect of face recognition and age estimation is better, and the visual effect and image quality are better.