Abstract
Nonuniform illumination is one of the main issues that hinder the accuracy of face recognition because it makes the intra-person variation more complicated. To minimize the intra-person differences caused by varying illuminations, this paper presents a normalization method based on Convolutional Auto-encoder (CAE). The CAE is employed to map the face images under various illumination conditions to a normalized one, generating preliminary results with blurry and insufficient facial details, which are tricky for recognition. To recover these details, a restoration method based on re-blurring strategy and frequency analysis is proposed to preserve the facial features lying in high-frequency components based on discrete cosine transform (DCT). Therefore, in our method, these components are extracted and re-introduced into the outputs of CAE to enhance the fidelity of outputs. Thus, the facial details are preserved to the largest degree and the following works such as recognition tasks are benefited. Experiments conducted on the AR, extended Yale B, and CAS-PEAL database demonstrate the effectiveness of our method.
Highlights
Face recognition has the potential to be widely applied in access control, identity authentication, watch-list surveillance etc
In this paper, we propose an illumination normalization approach based on Convolutional Auto-encoder (CAE) and discrete cosine transform (DCT) fusion
The CAE is used to obtain generally-normalized results while the DCT fusion based on iterative strategy compensate for its deficiency in image quality
Summary
Face recognition has the potential to be widely applied in access control, identity authentication, watch-list surveillance etc. By training the two GANs interactively, the output is premium in quality These methods [14]–[16] usually rely on carefully-designed network architectures or sophisticated loss functions, increasing computational cost. Park et al [23] achieve good performance in low-light image enhancement by utilizing two networks, including an AE for illumination estimation and a CAE for image restoration. A CAE combined with a detail restoration method is proposed for illumination normalization. The HF of the original image is extracted and combined with the LF of the generated image to achieve higher fidelity This strategy takes the advantages of both the CAE and traditional methods, and can be extended to alleviate the quality degradation problems in other similar fields.
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