Vol. 2 Issue 2
Year:2014
Issue:Jun-Aug
Title:Person Identification Using Face, Ear and Foot Modalities at Rank Level
Author Name:Snehlata Barde and A.S. Zadgaonkar
Synopsis:
Person recognition is a necessary requirement in government sector and private organization. AADHAR card is an application software used for person identification based on biometrics field since biometrics may be used to ensure that a person is authenticated. Multimodal biometric is a combination of two or more biometrics that helps to remove the limitation of a single biometric trait or an achievement of multimodal biometric person identification by means of combining deferent biometrics modalities like face, ear, iris, finger prints palm prints and foot. This paper worked on three modalities face, ear and foot for calculating results at rank level. For this, the authors have calculated Weight score of each modalities using different classifiers for face, PCA based neural network classifier, for Ear Eigen images and for foot modified sequential harr transform. After that the authors applied logistic regression method on fused data and calculated results that gave better result as compared to others. All works were performed on self created database of 100 persons.
Year:2014
Issue:Jun-Aug
Title:Person Identification Using Face, Ear and Foot Modalities at Rank Level
Author Name:Snehlata Barde and A.S. Zadgaonkar
Synopsis:
Person recognition is a necessary requirement in government sector and private organization. AADHAR card is an application software used for person identification based on biometrics field since biometrics may be used to ensure that a person is authenticated. Multimodal biometric is a combination of two or more biometrics that helps to remove the limitation of a single biometric trait or an achievement of multimodal biometric person identification by means of combining deferent biometrics modalities like face, ear, iris, finger prints palm prints and foot. This paper worked on three modalities face, ear and foot for calculating results at rank level. For this, the authors have calculated Weight score of each modalities using different classifiers for face, PCA based neural network classifier, for Ear Eigen images and for foot modified sequential harr transform. After that the authors applied logistic regression method on fused data and calculated results that gave better result as compared to others. All works were performed on self created database of 100 persons.
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