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Most of the current techniques for face recognition require the presence of afull face of the person to be recognized, and this situation is difficult toachieve in practice, the required person may appear with a part of his face,which requires prediction of the part that did not appear. Most of the currentforecasting processes are done by what is known as image interpolation, whichdoes not give reliable results, especially if the missing part is large. Inthis work, we adopted the process of stitching the face by completing themissing part with the flipping of the part shown in the picture, depending onthe fact that the human face is characterized by symmetry in most cases. Tocreate a complete model, two facial recognition methods were used to prove theefficiency of the algorithm. The selected face recognition algorithms that areapplied here are Eigenfaces and geometrical methods. Image stitching is theprocess during which distinctive photographic images are combined to make acomplete scene or a high-resolution image. Several images are integrated toform a wide-angle panoramic image. The quality of the image stitching isdetermined by calculating the similarity among the stitched image and originalimages and by the presence of the seam lines through the stitched images. TheEigenfaces approach utilizes PCA calculation to reduce the feature vectordimensions. It provides an effective approach for discovering thelower-dimensional space. In addition, to enable the proposed algorithm torecognize the face, it also ensures a fast and effective way of classifyingfaces. The phase of feature extraction is followed by the classifier phase.

Human Face Recognition from Part of a Facial Image based on Image Stitching