Recognizing Surgically Altered Face Images Using Multiobjective Evolutionary Algorithm

• Published in: IEEE transactions on information forensics and security Vol. 8; no. 1; pp. 89 - 100
• Main Authors: Bhatt, H. S., Bharadwaj, S., Singh, R., Vatsa, M.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.01.2013; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithmics. Computability. Computer arithmetics; Algorithms; Applied sciences; Artificial intelligence; Biological cells; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Data mining; Evolutionary algorithms; Exact sciences and technology; Face; Face recognition; Facial features; Feature extraction; genetic algorithm; granular computing; Granular materials; Granules; Memory and file management (including protection and security); Memory organisation. Data processing; Pattern recognition. Digital image processing. Computational geometry; Plastic surgery; Software; Surgery; Theoretical computing; Biometrics; Grain size; Computer vision; Image recognition; Forensic science; Image processing; Face recognition; Evolutionary algorithm; Image databank; Multiobjective programming; High precision; Plastic surgery; Modeling; Granular computing; Genetic algorithm; User interface; Facies; Selection criterion; System identification; Automatic recognition; Adulteration; Computer security
• ISSN: 1556-6013; 1556-6021
• DOI: 10.1109/TIFS.2012.2223684

Widespread acceptability and use of biometrics for person authentication has instigated several techniques for evading identification. One such technique is altering facial appearance using surgical procedures that has raised a challenge for face recognition algorithms. Increasing popularity of plastic surgery and its effect on automatic face recognition has attracted attention from the research community. However, the nonlinear variations introduced by plastic surgery remain difficult to be modeled by existing face recognition systems. In this research, a multiobjective evolutionary granular algorithm is proposed to match face images before and after plastic surgery. The algorithm first generates non-disjoint face granules at multiple levels of granularity. The granular information is assimilated using a multiobjective genetic approach that simultaneously optimizes the selection of feature extractor for each face granule along with the weights of individual granules. On the plastic surgery face database, the proposed algorithm yields high identification accuracy as compared to existing algorithms and a commercial face recognition system.