Face recognition by using wavelet-subband booster
Jing-Wein Wang, Tzu-Hsiung Chen · 2018
Lighting variation is a challenge for face recognition. This paper proposes a new enhancement method called waveletsubband booster, which can be used to redeem face image quality, to overcome this problem. The efficient brightness detector was used to classify the color face image into three classes of bright, normal, or dark. The RGB color channels of the face image were respectively transformed to the discrete wavelet domain. Each subband coefficients of the RGB color channels were then adjusted by multiplying the singular value matrices of these frequency subband coefficient matrices with the boosting coefficients. An image denoising model was further applied. Then the 2D inverse discrete wavelet transform was performed to obtain the boosted color face image without the lighting effect. The experimental results demonstrated the efficiency of the proposed methodology. The proposed method not only yield boosted images that are good as they were taken under normal lighting, but also significantly improve the accuracy and computation speed for face recognition.