Abstract
With technological innovations soaring high researchers are able to develop more efficient and adaptable biometric recognition systems. Biometric systems work by matching different biometric traits to templates in a database. Recognition systems use soft as well as hard biometrics. Soft biometrics are facial or birth marks, gender, race etc. whereas hard biometrics include fingerprint, iris, retina, face etc. In this paper we have proposed a methodology by clubbing an existing facial recognition system with the statistics attained from facial marks in order to mend the recognition rate between before after surgically altered images of human beings. Firstly an algorithm is introduced for spontaneously identifying facial marks, which are represented using Histograms of Oriented Gradients (HOG), secondly they are accorded to their location in the face image. The proposed methodology is implemented on the plastic surgical facial dataset. Widespread experiments are lead in order to show the efficacy of the anticipated facial mark detection process, and to substantiate the benefits of using the statistics of facial marks on top of customary face acknowledgement schemes. The above mentioned methodology is till date not implemented on surgically altered facial image samples.
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