Abstract
Plastic surgery procedures on the face introduce skin texture variations between images of the same person (intra-subject), thereby making the task of face recognition more difficult than in normal scenario. Usually, in contemporary face recognition systems, the original gray-level face image is used as input to the Gabor descriptor, which translates to encoding some texture properties of the face image. The texture-encoding process significantly degrades the performance of such systems in the case of plastic surgery due to the presence of surgically induced intra-subject variations. Based on the proposition that the shape of significant facial components such as eyes, nose, eyebrow, and mouth remains unchanged after plastic surgery, this paper employs an edge-based Gabor feature representation approach for the recognition of surgically altered face images. We use the edge information, which is dependent on the shapes of the significant facial components, to address the plastic surgery-induced texture variation problems. To ensure that the significant facial components represent useful edge information with little or no false edges, a simple illumination normalization technique is proposed for preprocessing. Gabor wavelet is applied to the edge image to accentuate on the uniqueness of the significant facial components for discriminating among different subjects. The performance of the proposed method is evaluated on the Georgia Tech (GT) and the Labeled Faces in the Wild (LFW) databases with illumination and expression problems, and the plastic surgery database with texture changes. Results show that the proposed edge-based Gabor feature representation approach is robust against plastic surgery-induced face variations amidst expression and illumination problems and outperforms the existing plastic surgery face recognition methods reported in the literature.
Highlights
The much attention given to face recognition within the research and commercial community can be associated with its real-world application potentials in areas such as surveillance, homeland security, and border control
The reader is referred to [57] for results on Labeled Faces in the Wild (LFW) database. We note at this point that the goal of the current paper is to demonstrate the efficiency of the proposed descriptor based face recognition method, not to compete in the LFW challenge
5 Conclusion We have presented the proposed edge-based Gabor feature representation approach for appearance representation of faces altered by plastic surgery procedures
Summary
The much attention given to face recognition within the research and commercial community can be associated with its real-world application potentials in areas such as surveillance, homeland security, and border control. Among the most challenging tasks for face recognition in these application scenarios is the development of robust face recognition systems [1] This implies that apart from recognizing faces under normal scenario, such systems should be able to successfully handle issues arising from unconstrained conditions. Like changes in illumination direction, plastic surgery procedures induce intra-subject (face image versions of the same person) dissimilarity, which are impediments to robust face recognition. Such problem can be exacerbated when other conditions such as pose and expression are included. The main focus of this paper is to address the recognition problems that arise from conditions where the face is surgically altered
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