Novel Face Recognition Framework for Plastic Surgery and Unconstrained Facial Datasets
Manjiri Arun Ranjanikar1, U V Kulkarni2
1Manjiri Arun Ranjanikar, Department of Computer Science and Engineering, SGGS Institute of Engineering and Technology, Maharashtra, India.
2U V Kulkarni, Department of Computer Science and Engineering, SGGS Institute of Engineering and Technology, Maharashtra, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 1442-1450 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8119088619/2019©BEIESP | DOI: 10.35940/ijeat.F8119.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Since from the last decade, the importance of efficient and robust face recognition becomes interesting research problem due to its growing demand in user authentication process across different applications. Several face recognition method designed by considering the challenges of constrained and unconstrained face acquisition. The performance of face recognition systems becomes worst while working with unconstrained and plastic surgery datasets. The unconstrained dataset may contain the significant variations in illumination, expressions, and face image quality. This paper proposed the novel face recognition framework which is built on top of hybrid face descriptor and hybrid features extraction technique. The purpose of hybrid face descriptor is to achieve the bridge the gap between spatial information and histogram representations to address the challenges of unconstrained face conditions efficiently. The hybrid features extraction includes the histogram features (single and multi-level) and invariant features. Since the moment invariant features become effective shape descriptor to address the challenges of unconstrained face images, we proposed 11 different invariant moments from the hybrid face descriptor. To normalize and reduce the features, we applied lightweight features selection technique. The experimental result shows that proposed face recognition framework improves the overall accuracy different datasets.
Keywords: Face recognition, face descriptor, dual cross pattern, moment invariant features, histogram features, features selection, classification.