Eigenface Based Automatic Facial Feature Tagging

There are several approaches to search databases of faces. However such methods still require a significant use of humans to interpret an eyewitness account and so forth. In many cases these searches are done using visual building tools as creating a graphical face model. A system that can easily interface with general users should directly search a person by description given verbally or textually. This would reduce costs in the search process. Facial feature characteristics identification would act as a stepping stone in cataloguing large face databases automatically thus providing the possibility of a description based face search by text. This paper presents the possibility of utilizing eigenface approach to recognize different characteristics of a facial feature and assigning descriptive words such as "Large", "Small" to each feature. After training the system, it would automatically attempt to match a pattern in the training set that best describes the input image and output a tag associated with it. This effectively allows an image of a person\´s face to be tagged by his or her feature characteristics. While utilizing the standard set steps as defined in the eigenface algorithm, slight modifications are done in the algorithm that matches input images with ones in the training set. The training set defined has a very huge impact for the final outcome, and due to the subjective nature of the training, future research would be done on this regard. The investigation showed that the method works fine with well defined features such as eyes but fails for features such as foreheads due to the lack of significant differences or characteristics between such features. Hence it is seen that while eigenface can be used for the categorization of well defined features, it is unable by itself to create a system that can cover all features of a face.