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

TL;DR

This paper introduces a face recognition method that reconstructs missing facial parts by stitching and flipping based on facial symmetry, improving recognition accuracy when only partial faces are available.

Contribution

The work proposes a novel face reconstruction technique using image stitching and flipping, combined with Eigenfaces and geometrical methods for enhanced recognition of partial faces.

Findings

Effective face reconstruction from partial images.

Improved recognition accuracy with the proposed stitching method.

Validation using Eigenfaces and geometrical recognition methods.

Abstract

Most of the current techniques for face recognition require the presence of a full face of the person to be recognized, and this situation is difficult to achieve 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 current forecasting processes are done by what is known as image interpolation, which does not give reliable results, especially if the missing part is large. In this work, we adopted the process of stitching the face by completing the missing part with the flipping of the part shown in the picture, depending on the fact that the human face is characterized by symmetry in most cases. To create a complete model, two facial recognition methods were used to prove the efficiency of the algorithm. The selected face recognition algorithms that are applied here are Eigenfaces and geometrical methods. Image stitching is the process during which distinctive photographic images are combined to make a complete scene or a high-resolution image. Several images are integrated to form a wide-angle panoramic image. The quality of the image stitching is determined by calculating the similarity among the stitched image and original images and by the presence of the seam lines through the stitched images. The Eigenfaces approach utilizes PCA calculation to reduce the feature vector dimensions. It provides an effective approach for discovering the lower-dimensional space. In addition, to enable the proposed algorithm to recognize the face, it also ensures a fast and effective way of classifying faces. The phase of feature extraction is followed by the classifier phase.