Keywords- Family Similarity Recognition, Facial Feature Extraction, Face Detection, PCA, Image Database.
Over the last thirty years or so, face recognition is an active research area in computer vision and one of the most successful applications of image analysis and understanding. Different algorithms have been proposed by researchers for solving this problem and many of them are considered as the state of the art. Nowadays, the face recognition problem is not only important in the research area, but it is vital in the commercial applications.
A general statement of the face recognition problem can be formulated as follows: Given still or video images of a scene, identify or verify one or more persons in the scene using a stored database of faces. Due to this definition well-known algorithms such as PCA , ICA , LDA , EBGM , B...
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...e considered for it. For example, humans use terms such as "entire face of person A is similar to person B" or "eyes and eyebrows of person A are similar to person B" or "profile view of person A is similar to person B" to express the similarity criteria between people. With respect to these observations, three features are utilized in the proposed method include: "The Whole Face", "The Facial Features’ Perimeter" and "The Ratio between Facial Features". These features are selected from the frontal and side view images. The facial features’ perimeter includes forehead, eyebrows and eyes, nose, mouth, chin and cheek. The ratios between facial features’ point can be calculated from the distances that are evident in Figure 1. Utilizing ratio instead of distances between the facial features' point eliminates the dependency to the image scale. These ratios are as follow:
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