Face Recognition by using Feed Forward Back Propagation Neural Network
نویسنده
چکیده
Face recognition technology has seen dramatic improvements in performance over the past decade, and such systems are now widely used for security and commercial applications. In this paper an improved approach has been used in which feed forward back propagation neural network is implemented through matlab. The results obtained shows that the proposed approach somewhat improves the performance compared to the previous approaches.
منابع مشابه
Handwritten Character Recognition using Modified Gradient Descent Technique of Neural Networks and Representation of Conjugate Descent for Training Patterns
The purpose of this study is to analyze the performance of Back propagation algorithm with changing training patterns and the second momentum term in feed forward neural networks. This analysis is conducted on 250 different words of three small letters from the English alphabet. These words are presented to two vertical segmentation programs which are designed in MATLAB and based on portions (1...
متن کاملMinutiae Based Thermal Human Face Recognition using Label Connected Component Algorithm
In this paper, a thermal infra red face recognition system for human identification and verification using blood perfusion data and back propagation feed forward neural network is proposed. The system consists of three steps. At the very first step face region is cropped from the colour 24-bit input images. Secondly face features are extracted from the croped region, which will be taken as the ...
متن کاملSignal Prediction by Layered Feed - Forward Neural Network (RESEARCH NOTE).
In this paper a nonparametric neural network (NN) technique for prediction of future values of a signal based on its past history is presented. This approach bypasses modeling, identification, and parameter estimation phases that are required by conventional parametric techniques. A multi-layer feed forward NN is employed. It develops an internal model of the signal through a training operation...
متن کاملComparison of artificial neural network and multivariate regression methods in prediction of soil cation exchange capacity (Case study: Ziaran region)
Investigation of soil properties like Cation Exchange Capacity (CEC) plays important roles in study of environmental reaserches as the spatial and temporal variability of this property have been led to development of indirect methods in estimation of this soil characteristic. Pedotransfer functions (PTFs) provide an alternative by estimating soil parameters from more readily available soil data...
متن کاملFace Recognition System Using Back Propagation Artificial Neural Networks
The problem in face recognition is to find the best match of an unknown image against a database of face models or to determine whether it does not match any of them well. In this method, we use back propagation neural network for implementation. It is an information processing system that has been developed as a generalization of the mathematical model of human recognition. The function of a n...
متن کامل