Face recognition is a challenging problem due to different illuminations, poses, facial expressions, and occlusions. In this paper, a new robust face recognition method is proposed based on the color and edge orientation difference HISTOGRAM. Firstly, the color and edge orientation difference HISTOGRAM is extracted using color, color difference, edge orientation, and edge orientation difference of the face image. Then the backward feature selection is employed in order to reduce the number of features. Finally, the Canberra measure is used to assess the similarity between the images. The color and edge orientation difference HISTOGRAM shows the color and edge orientation difference between two neighboring pixels. This HISTOGRAM is effective for face recognition due to the different skin colors and different edge orientations of the face image, which leads to a different light reflection. The proposed method is evaluated on the Yale and ORL face datasets. These datasets consist of gray-scale face images under different illuminations, poses, facial expressions, and occlusions. The recognition rate over the Yale and ORL datasets is achieved to be 100% and 98. 75%, respectively. The experimental results demonstrate that the proposed method outperforms the existing methods in face recognition.

Face recognition under uneven illumination is still an open problem. One of the main challenges in real-world face recognition systems is illumination variation. In this paper, a novel illumination invariant face recognition approach base on Self Quotient Image (SQI) and weighted Local Binary Pattern (WLBP) HISTOGRAM has been proposed. In this system, the performance of the system is increased by using different sigma values of SQI for training and testing. Furthermore, using two multi-region uniform LBP operators for feature extraction simultaneously, made the system more robust to illumination variation. This approach gathers information of the image in different local and global levels. The weighted Chi square statistic is used for HISTOGRAM comparison and NN (1-NN) is used as classifier. The weighted approach emphasizes on the more important regions in the faces. The proposed approach is compared with some new and traditional methods like QI, SQI, QIR, MQI, DMQI, DSFQI, PCA and LDA on Yale face database B and CMU-PIE database. The experimental results show that the proposed method outperforms other tested methods.

In this paper, a method is proposed to automatically select reference image in HISTOGRAM matching. HISTOGRAM matching is one of the simplest spatial image enhancement methods which improves contrast of the initial image based on HISTOGRAM of the reference image. In the conventional HISTOGRAM matching methods, user should perform several experiments on various images to find a suitable reference image. This paper presents a new method to automatically select the reference image. In this method, images are converted from RGB to HSV, and the illumination (V) components are considered to select the reference image. The appropriate reference image is selected using a similarity measure via measuring the similarity between the HISTOGRAMs of the initial image and HISTOGRAMs of the images in the data base. Indeed, an image with similar HISTOGRAM to the HISTOGRAM of the original images is more appropriate to choose as the reference image for HISTOGRAM matching. Results in this research indicate superiority of the proposed approach, compared to other existing approaches, in image enhancement via HISTOGRAM matching. In addition, the user would have no concern in selecting an appropriate reference image for HISTOGRAM matching in the proposed approach. This approach is applicable to both RGB and gray scale images.

In this paper, an adaptive image contrast enhancement algorithm based on an optimization problem in two dimensional HISTOGRAM domain is presented. To reduce the unwanted effects of the HISTOGRAM adjustment, through this optimization-similar to the other methods- the 2D HISTOGRAM of enhanced image is found in close proximity to input image HISTOGRAM and uniform distribution, simultaneously. In addition, different from the other methods, by adaptive adjusting the components of a weight matrix, local information is counted. Experimental results in the quantitative and qualitative assessments on a wide range of images demonstrate the performance of the proposed method. Tests have shown that with the addition of the adaptive adjusting the weights, the average performance in contrast enhancement increases 75 and 3 percent from the viewpoint of the AMBE_N and DE_N, respectively.