A New Neural Network Approach for Face Recognition based on Conjugate Gradient Algorithms and Principal Component Analysis
Publish Year: 1391
نوع سند: مقاله کنفرانسی
زبان: English
View: 1,589
This Paper With 7 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
این Paper در بخشهای موضوعی زیر دسته بندی شده است:
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
ICNMO01_121
تاریخ نمایه سازی: 19 اسفند 1391
Abstract:
This paper presents a new approach based on conjugate gradient algorithms (CGAs) and principal component analysis (PCA) for face recognition. First, images are decomposed into a set of time-frequency coefficients using discrete wavelet transform (DWT). Basic back propagation (BP) is a well established technique in training a neural network. However, since in this algorithm the steepest descent direction is not the quickest convergence, it is slow for many practical problems and in many cases including face recognition, its performance is not satisfactory. To overcome this problem, four algorithms, namely, Fletcher-Reeves CGA, Polak-Ribikre CGA, Powell-Beale CGA, and scaled CGA have been proposed. Also, in this paper the PCA as a pre-processing step to create the uncorrelated and distinct features of the DWT of images is used. The simulation results show that all of the proposed methods, compared with the basic BP, have greater accuracies
Keywords:
Face recognition , discrete wavelet transform , conjugate gradient algorithm , and principal component analysis
Authors
Hamed Azami
Department of Electrical Engineering, Iran University of Science and Technology,
Milad Malekzadeh
Department of Electrical and Computer Engineering, Babol Industrial University,
Saeid Sanei
Faculty of Engineering and Physical Sciences, University of Surrey,
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :