Age-Variant Face Recognition Scheme Using Scale Invariant Feature Transform and the Probabilistic Neural Network

  • 陳 李永

Student thesis: Master's Thesis


Facing to the aging variation problem how to improve the correct recognition rate of an automatic face recognition system is an important issue Most face recognition studies only focus on aging simulation or age estimation For face recognition system under age variation it is possible to effectively design a suitable and efficient performance matching a framework model This thesis mainly discusses the differences caused by age level using the Scale Invariant Feature Transform (SIFT) algorithm Because it has a high tolerance of noise characteristics the light and viewing angle has changed It can be detected and can describe local features of the face images through intensively sampling a local descriptor Then it uses the Probabilistic Neural Network (PNN) by Bayesian classification decisions to deal with the problem by adjusting the smoothing parameter from the probabilistic density function in order to improve the recognition success rate Finally the proposed age-variant face recognition scheme is applied to the FG-NET (Face and Gesture Recognition Research Network) face database and the simulation results demonstrate that the correct recognition rate is indeed improved
Date of Award2014 Sept 4
Original languageEnglish
SupervisorTzuu-Hseng S. Li (Supervisor)

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