Illumination compensation for facial feature point localization in a single 2D face image

Current researches have demonstrated that illumination variation on face images degrades the accuracy of facial identity and emotion recognition. To decrease the impact of illumination variation, researchers have proposed many creative methods of illumination compensation. However, these methods are limited in compensating for the shadow around the nose. On the basis of our previous researches, we now propose a novel approach which can effectively decrease the impact of illumination variation, especially the shadow around the nose. Firstly, we preprocessed the face image with uneven brightness using technologies of illuminant direction estimation and improved Retinex. Secondly, we turn the original face image into a binary image with only shadow region or non-shadow region using region growing technology. Thirdly, we calculate the difference between the intensity of the original input face image and the average intensity of the face images under the frontal illumination. Fourthly, for the face image preprocessed in the first step, we keep its non-shadow region. For the intensity difference, we extract its shadow region whose intensity is reduced by an adaptive value. Fifthly, we synthesize the non-shadow region and the shadow region in step four. Finally, we apply maximum filter to smooth the boundary between them. The proposed method is simple in computation and does not need any training steps or any knowledge of 3D models. The experimental results using extended Yale face database B show that our method achieves better illumination compensation comparing with the existing techniques, and provide more satisfactory experimental data for facial identity and emotion recognition.