International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 03 | Mar -2017
p-ISSN: 2395-0072
www.irjet.net
RECOGNITION OF SURGICALLY ALTERED FACE IMAGES D Souza Janice Rachel, D Mello Marina, Choudhary Sangeeta, Mrs. S.M. Jagdale Dept. of Electronics and Telecommunication, Bharati Vidyapeeth’s College of Engineering for Women, Pune, Maharashtra, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Plastic surgery procedures provide a
encourages individuals to undergo plastic surgery for cosmetic reasons like nose jobs, skin up lifting, etc.
procuring way to enhance the facial appearance by correcting efficient and feature anomalies and treating facial skin to get a younger look. The effects of change in illumination direction, plastic surgery procedures induce intra face (face image versions of the same person) dissimilarity, which are obstruction to robust face recognition. The most challenging task for face recognition in these application scenarios is the development of robust face recognition systems. In this research, we have designed a multiple granular algorithm to match face images of a person before and after his plastic surgery. A total of 40 face granules are extracted from each of the images and the granules are compared. Based on the comparison of the two images a decision is made.
The multi objective granular algorithm first generates non-disjoint face granules from two levels of granularity. The granular information is processed using a multi objective genetic approach that simultaneously optimizes the selection of feature extractor for each face granule along with Genetic algorithm. [2] On the plastic surgery face database, the proposed algorithm by this approach yields high identification accuracy as compared to existing algorithms and a commercial face recognition system. Face recognition algorithms either use facial information in a holistic way or extract features and process the min parts. In the presence of variations such as change in expressions, pose made for a photograph, difference in illumination, and disguise, it is observed that local facial regions are more resilient and can therefore be used for efficient face recognition.
Key Words: Face recognition, Plastic surgery, genetic algorithm, granular computing.
Face granules are extracted using two different levels of granularity. The first level provides global information at multiple resolutions, i.e. the granules are extracted to account for wrinkles, lines , edges, etc. [3] This is analogous to a human mind processing holistic information for face recognition at varying resolutions. From the discoveries of Campbell, the inner and outer facial information are extracted at these levels. Features local to the face play an important role in face recognition by human mind. Therefore, at the second level, features are extracted from the local facial regions. We have proposed a multi objective evolutionary genetic algorithm is proposed for feature selection and weight optimization for each face granule. [4] The selection of feature extractors allows switching between two feature extractors (SIFT and EUCLBP) and helps in encoding discriminatory information for each face granule. Detailed analysis of the contribution of two granular levels and individual face granules combines the hypothesis that the proposed algorithm combines diverse information from all granules and addresses the nonlinear variations in pre and post surgery images.
1.INTRODUCTION Widespread use of biometrics for person authentication has instigated several techniques for evading identification of a person. One very famous technique is altering facial appearance using surgical procedures that has raised a challenge for the existing face recognition algorithms. The ever increasing popularity of plastic surgery and its effects on automatic face recognition has attracted attention from the research community for security reasons. However, the nonlinear variations derived from plastic surgery remain difficult to be modeled by existing face recognition systems and need to be modified. [1] In this research, we have implemented a multiple level granular algorithm to match the face images of a person before and after plastic surgery. Surgical procedures amend the facial features and skin texture thereby providing a change in the appearance of face. Reduction in time and cost required for these procedures, the popularity of altering the face using plastic surgery is increasing. The widespread acceptability in the society
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