Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Based on geometrical facial features and image segmentation, this paper presents a novel algorithm for automatic localization of human eyes in grayscale still images with complex backgrounds. First of all, an eye location determination criterion is established by the a priori knowledge of geometrical facial features. Secondly, a range of threshold values that would separate eye blocks from others in a segmented facial image is estimated from the facial image histogram. Thirdly, with the progressive increase of the threshold by an appropriate step in that range, the size of the existing blocks in the segmented facial image will expand, some existing blocks will merge into one block, and some new blocks will emerge. Once two eye blocks appear from the segmented image, they will be detected by the eye location determination criterion. Finally, the 2D correlation coefficient is used as a symmetry similarity measure to check the factuality of the two detected eyes. In this way, the optimal threshold value can be automatically found, based on the detection result, such that eyes can be accurately located. The experimental results demonstrate the high efficiency of the algorithm in runtime and its correct localization rate.
['Liang Tao', 'H.K. Kwan']
I am writing a research paper on a idea Automatic localization of human eyes in complex background
[{"from": "human", "value": "I am writing a research paper on a idea Automatic localization of human eyes in complex background"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Based on geometrical facial features and image segmentation, this paper presents a novel algorithm for automatic localization of human eyes in grayscale still images with complex backgrounds. First of all, an eye location determination criterion is established by the a priori knowledge of geometrical facial features. Secondly, a range of threshold values that would separate eye blocks from others in a segmented facial image is estimated from the facial image histogram. Thirdly, with the progressive increase of the threshold by an appropriate step in that range, the size of the existing blocks in the segmented facial image will expand, some existing blocks will merge into one block, and some new blocks will emerge. Once two eye blocks appear from the segmented image, they will be detected by the eye location determination criterion. Finally, the 2D correlation coefficient is used as a symmetry similarity measure to check the factuality of the two detected eyes. In this way, the optimal threshold value can be automatically found, based on the detection result, such that eyes can be accurately located. The experimental results demonstrate the high efficiency of the algorithm in runtime and its correct localization rate."}]