FACE SEGMENTATION
INTRODUCTION
1.1 Aim of the Thesis
This project presents an interactive algorithm to automatically segment out a person’s face from a given image that consists of a head-and-shoulders view of the person and a complex background scene. The method involves a fast, reliable, and effective algorithm that exploits the spatial distribution characteristics of human skin color. To fulfill this aim, the following objectives are carried out:
1. Implement different types of image segmentations.
2. A universal skin-color map is derived and used on the chrominance component of the input image to detect pixels with skin-color appearance .
3. Then, based on the spatial distribution of the detected skin-color pixels and their corresponding luminance values, the algorithm employs a set of novel regularization processes to reinforce regions of skin color pixels that are more likely to belong to the facial regions and eliminate those that are not.
4. The performance of the face segmentation algorithm is illustrated by some simulation results carried out on various head-and-shoulders test images.
1.2 Scope of the Thesis
The main objective of this research is to design a system that can find a person’s face from given image data. This problem is commonly referred to as face location, face extraction, or face segmentation. Regardless of the terminology, they all share the same objective. However, note that the problem usually deals with finding the position and contour of a person’s face since its location is unknown, but given the knowledge of its existence. If this is not known, then there is also a need to discriminate between “images containing faces” and “images not containing faces.” This is known as face detection. This paper, however, focuses on face segmentation. The significance of this problem can be illustrated by its vast applications, as face segmentation holds an important key to future advances in human-to-human and human-to-machine communications. The
1.1 Aim of the Thesis
This project presents an interactive algorithm to automatically segment out a person’s face from a given image that consists of a head-and-shoulders view of the person and a complex background scene. The method involves a fast, reliable, and effective algorithm that exploits the spatial distribution characteristics of human skin color. To fulfill this aim, the following objectives are carried out:
1. Implement different types of image segmentations.
2. A universal skin-color map is derived and used on the chrominance component of the input image to detect pixels with skin-color appearance .
3. Then, based on the spatial distribution of the detected skin-color pixels and their corresponding luminance values, the algorithm employs a set of novel regularization processes to reinforce regions of skin color pixels that are more likely to belong to the facial regions and eliminate those that are not.
4. The performance of the face segmentation algorithm is illustrated by some simulation results carried out on various head-and-shoulders test images.
1.2 Scope of the Thesis
The main objective of this research is to design a system that can find a person’s face from given image data. This problem is commonly referred to as face location, face extraction, or face segmentation. Regardless of the terminology, they all share the same objective. However, note that the problem usually deals with finding the position and contour of a person’s face since its location is unknown, but given the knowledge of its existence. If this is not known, then there is also a need to discriminate between “images containing faces” and “images not containing faces.” This is known as face detection. This paper, however, focuses on face segmentation. The significance of this problem can be illustrated by its vast applications, as face segmentation holds an important key to future advances in human-to-human and human-to-machine communications. The