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-age-invariant-face-recognition

-->PCA-for dimensionality reduction. -->LBP- it's efficient in the illumination, rotation, and grayscale variance -->WHT- Walsh-Hadamard Transform(WHT) for orthogonal transformation is used for feature extraction. Before applying LBP here I applied the first Walsh-Hadamard Transform (WHT) for orthogonal transformation. -->link of the paper for whole process flow: https://www.researchgate.net/profile/Priyanka_Patel8/publication/315892151_Investigate_Age_Invariant_Face_Recognition_Using_PCA_LBP_Walsh_Hadamard_Transform_with_Neural_Network/links/58ec89e40f7e9b6b274bb17c/Investigate-Age-Invariant-Face-Recognition-Using-PCA-LBP-Walsh-Hadamard-Transform-with-Neural-Network.pdf To learn more about the background of the dataset, and the AIFR you must visit the : https://scholar.google.com/scholar?cluster=3626589220394005192&hl=en&as_sdt=2005 You must cite this paper if you use: Patel, P., and A. Ganatra. "Investigate age invariant face recognition using PCA, LBP, Walsh Hadamard transform with a neural network." International Conference on Signal and Speech Processing (ICSSP-14). 2014.

aifr

In recent years, face recognition has emerged as an important problem in the domain of computer vision. FR has become a popular area for research, mainly due to increasing security demands and its potential, commercial and law enforcement applications. Hence, this paper focuses on developing a technique that provides a solution for an efficient face recognition system in different applications. Such as including biometric verification, passport verification, cybercrime, find missing person, etc. As compared with other biometrics systems using fingerprint/ palm-print and iris, face has distinct advantages because of its non-contact process. Here mainly three techniques were proposed and implemented. Feature extraction was achieving using Walsh Hadamard Transform & Local Binary.

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