Nilesh Pawar - Academia.edu (original) (raw)
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Graduate Center of the City University of New York
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Various biometric traits, such as fingerprint, face, and iris, have been exclusively used in appl... more Various biometric traits, such as fingerprint, face, and iris, have been exclusively used in applications of person verification and identification. Current research in biometrics includes making the verification system more robust and reliable. An existing way of acquiring the biometric sample from the subject needs cooperation from the subject under a controlled environment. However, capturing a sample in a controlled environment may have constraints of slower identification process, disturbance in person’s movement in public places, and need of extra supervising personnel. One of the main dominant covariates in unconstrained biometrics of the face is the nonuniform illumination across the face sample. In this paper, we have analyzed the effect of nonuniform lighting conditions on the face samples to be recognized. The experiments are performed for different subspace representations of the facial image. The inferences about the effectiveness of feature representation and illumination normalization techniques derived from the experimental observations are useful in developing the face-based unconstrained biometrics.
2017 International Conference On Smart Technologies For Smart Nation (SmartTechCon), 2017
Reliability and robustness of a verification system is a major concern in biometrics. One of the ... more Reliability and robustness of a verification system is a major concern in biometrics. One of the most important covariates in face non-cooperative biometrics is misalignment, which exist because of errors in face detector. Current research is happening in this direction in order to make non-cooperative biometrics more reliable and consistent. In this paper, we have proposed the face normalization method to overcome the negative effects of misalignment. This method is based on first determining the eye distance and then determining the face which depend on the location of eyes. With this method, the recognition accuracy increases significantly from 44.95% to 66.66 %. It is worth mentioning that the experiments were conducted on the dataset, where large variations of poses, scales and illumination exists and these characteristics makes this dataset more appropriate for doing validation for non-cooperative face biometrics. However, detection rate with eye detector will be smaller than that of face detector. Thus, it's essential to make eye detector more reliable and robust in non-cooperative face biometrics.
International Journal of Research in Engineering and Technology, 2014
Various biometric traits, such as fingerprint, face, and iris, have been exclusively used in appl... more Various biometric traits, such as fingerprint, face, and iris, have been exclusively used in applications of person verification and identification. Current research in biometrics includes making the verification system more robust and reliable. An existing way of acquiring the biometric sample from the subject needs cooperation from the subject under a controlled environment. However, capturing a sample in a controlled environment may have constraints of slower identification process, disturbance in person’s movement in public places, and need of extra supervising personnel. One of the main dominant covariates in unconstrained biometrics of the face is the nonuniform illumination across the face sample. In this paper, we have analyzed the effect of nonuniform lighting conditions on the face samples to be recognized. The experiments are performed for different subspace representations of the facial image. The inferences about the effectiveness of feature representation and illumination normalization techniques derived from the experimental observations are useful in developing the face-based unconstrained biometrics.
2017 International Conference On Smart Technologies For Smart Nation (SmartTechCon), 2017
Reliability and robustness of a verification system is a major concern in biometrics. One of the ... more Reliability and robustness of a verification system is a major concern in biometrics. One of the most important covariates in face non-cooperative biometrics is misalignment, which exist because of errors in face detector. Current research is happening in this direction in order to make non-cooperative biometrics more reliable and consistent. In this paper, we have proposed the face normalization method to overcome the negative effects of misalignment. This method is based on first determining the eye distance and then determining the face which depend on the location of eyes. With this method, the recognition accuracy increases significantly from 44.95% to 66.66 %. It is worth mentioning that the experiments were conducted on the dataset, where large variations of poses, scales and illumination exists and these characteristics makes this dataset more appropriate for doing validation for non-cooperative face biometrics. However, detection rate with eye detector will be smaller than that of face detector. Thus, it's essential to make eye detector more reliable and robust in non-cooperative face biometrics.
International Journal of Research in Engineering and Technology, 2014