Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Human-robot interaction relies heavily on the ability to locate and track agents in real-time. For joint attention [1] participating agents must be able to locate one another. Therefore, face detection (FD) and consequent tracking represents an area of great importance for interaction. Current approaches for FD, such as the Viola-Jones (VJ) method [3] present a robust, yet relatively slow method. Other approaches based on it use depth information to determine the possible search area for every pixel in the image, or in the post-detection step to eliminate false positives [4]. ,  investigate if depth-based image preprocessing can significantly reduce the search area, loring the number of false positives while shortening the execution time. Depth and RGB images of both sitting and standing people are recorded and then processed offline using the algorithm presented in this article. Images are acquired using Microsoft Kinect. The user is kept at a fixed distance from the camera (beten 1 and 1.5 meters). Hover, with slight modifications, the proposed algorithm can also be used with people at various distances. The processing is performed on a laptop with Intel T5750 processor and a Kinect. A sample output from RGB and IR cameras is shown in Fig. 1.
['Sasa Bodiroza']
I am writing a research paper on a idea Using image depth information for fast face detection
[{"from": "human", "value": "I am writing a research paper on a idea Using image depth information for fast face detection"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Human-robot interaction relies heavily on the ability to locate and track agents in real-time. For joint attention [1] participating agents must be able to locate one another. Therefore, face detection (FD) and consequent tracking represents an area of great importance for interaction. Current approaches for FD, such as the Viola-Jones (VJ) method [3] present a robust, yet relatively slow method. Other approaches based on it use depth information to determine the possible search area for every pixel in the image, or in the post-detection step to eliminate false positives [4]. ,  investigate if depth-based image preprocessing can significantly reduce the search area, loring the number of false positives while shortening the execution time. Depth and RGB images of both sitting and standing people are recorded and then processed offline using the algorithm presented in this article. Images are acquired using Microsoft Kinect. The user is kept at a fixed distance from the camera (beten 1 and 1.5 meters). Hover, with slight modifications, the proposed algorithm can also be used with people at various distances. The processing is performed on a laptop with Intel T5750 processor and a Kinect. A sample output from RGB and IR cameras is shown in Fig. 1."}]