Face recognition via edge-based Gabor feature representation for plastic surgery-altered images

Chude-Olisah, Chollette C; Sulong, Ghazali; Chude-Okonkwo, Uche A K; Hashim, Siti Z M

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.

• Journal Article Type: Article
• Acceptance Date: May 21, 2014
• Online Publication Date: Jul 5, 2014
• Publication Date: Jul 5, 2014
• Deposit Date: Jul 8, 2021
• Publicly Available Date: Jul 9, 2021
• Journal: EURASIP Journal on Advances in Signal Processing
• Electronic ISSN: 1687-6180
• Publisher: SpringerOpen
• Peer Reviewed: Peer Reviewed
• Volume: 2014
• Issue: 1
• Article Number: 102
• DOI: https://doi.org/10.1186/1687-6180-2014-102
• Public URL: https://uwe-repository.worktribe.com/output/7514615
• Publisher URL: https://asp-eurasipjournals.springeropen.com/
• Additional Information: Received: 30 July 2013; Accepted: 21 May 2014; First Online: 5 July 2014