Abstract
An illumination normalization method for face recognition has been developed since it was difficult to control lighting conditions efficiently in the practical applications. Considering that the irradiation light is of little variation in a certain area, a mean estimation method is used to simulate the illumination component of a face image. Illumination component is removed by subtracting the mean estimation from the original image. In order to highlight face texture features and suppress the impact of adjacent domains, a ratio of the quotient image and its modulus mean value is obtained. The exponent result of the ratio is closely approximate to a relative reflection component. Since the gray value of facial organs is less than that of the facial skin, postprocessing is applied to the images in order to highlight facial texture for face recognition. Experiments show that the performance by using the proposed method is superior to that of state of the arts.
Highlights
Face recognition is one of the most active research focuses due to its wide applications [1, 2]
Considering that the irradiation light is of little variation in a certain area, a mean estimation method is used to simulate the illumination component of a face image
In order to build a fair comparison with state of the arts, all these investigated methods are implemented with the parameters as the authors recommended
Summary
Face recognition is one of the most active research focuses due to its wide applications [1, 2]. Among numerous adverse factors for face recognition, appearance variation caused by illumination is one of the major problems which remain unsettled. The main drawbacks of the approaches mentioned above are the need of knowledge about the light source or a large volume of training data To overcome this demerit, region-based image preprocessing methods are proposed in [5,6,7]. Considering that the irradiation light is of little variation in a certain area, a mean estimation method is used to simulate the illumination component of a face image. The first contribution of the developed approach is that the performance is more robust in processing illumination variation for face recognition than that of state-of the arts. The second contribution is that the proposal can get some distinctive facial texture features for face recognition.
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