Face recognition system using neural network with Gabor and discrete wavelet transform parameterization

2014 ◽  
Author(s):  
Fatma Zohra Chelali ◽  
Amar Djeradi
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Recognition System ◽  
Discrete Wavelet ◽  
Face Recognition System

scholarly journals Hand gesture recognition using discrete wavelet transform and convolutional neural network

2020 ◽  
Vol 9 (3) ◽  
pp. 996-1004 ◽  
Author(s):  
Muhammad Biyan Priatama ◽  
Ledya Novamizanti ◽  
Suci Aulia ◽  
Erizka Banuwati Candrasari
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Convolutional Neural Network ◽  
Discrete Wavelet Transform ◽  
Gesture Recognition ◽  
Public Services ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Discrete Wavelet

Public services are available to all communities including people with disabilities. One obstacle that impedes persons with disabilities from participating in various community activities and enjoying the various public services available to the community is information and communication barriers. One way to communicate with people with disabilities is with hand gestures. Therefore, the hand gesture technology is needed, in order to facilitate the public to interact with the disability. This study proposes a reliable hand gesture recognition system using the convolutional neural network method. The first step, carried out pre-processing, to separate the foreground and background. Then the foreground is transformed using the discrete wavelet transform (DWT) to take the most significant subband. The last step is image classification with convolutional neural network. The amount of training and test data used are 400 and 100 images repectively, containing five classes namely class A, B, C, # 5, and pointing. This study engendered a hand gesture recognition system that had an accuracy of 100% for dataset A and 90% for dataset B.

VARIANT POSE FACE RECOGNITION USING DISCRETE WAVELET TRANSFORM AND LINEAR REGRESSION

2012 ◽  
Vol 26 (06) ◽  
pp. 1256013 ◽  
Author(s):  
SEYED OMID SHAHDI ◽  
SYED A. R. ABU-BAKAR
Keyword(s):  
Face Recognition ◽  
Wavelet Transform ◽  
Linear Regression ◽  
Discrete Wavelet Transform ◽  
Head Rotation ◽  
Recognition System ◽  
Discrete Wavelet ◽  
Limited Region ◽  
The Face ◽  
Pose Variation

Face recognition in constraint conditions is no longer a further challenge. However, even the best method is not able to cope with real world situations. In this paper, a robust method is proposed such that the performance of the face recognition system is still highly reliable even if the face undergoes large head rotation. Our proposed method considers local regions from half side of face rather than using the holistic face approach since in the former approach the "linearity" of features within the limited region is somewhat preserved regardless of the pose variation. Discrete wavelet transform is then utilized onto these patches in order to form face feature vectors. We train our recognizer using linear regression algorithm to interpret the relationship between a face vector for a specific pose and its corresponding frontal face feature vector. We demonstrate that our proposed method is able to recognize a non-frontal face with high accuracy even under low-resolution image by relying only on single frontal face in the database.

Maximal Overlap Discrete Wavelet Transform, Graph Theory And Backpropagation Neural Network In Stock Market Forecasting

2018 ◽  
Vol 5 (1) ◽  
pp. 41-46
Author(s):  
Rosalina Rosalina ◽  
Hendra Jayanto
Keyword(s):  
Neural Network ◽  
Graph Theory ◽  
Wavelet Transform ◽  
Stock Market ◽  
Discrete Wavelet Transform ◽  
Stock Price ◽  
Moving Average ◽  
Discrete Wavelet ◽  
Stock Market Forecasting ◽  
Artificial Neural

The aim of this paper is to get high accuracy of stock market forecasting in order to produce signals that will affect the decision making in the trading itself. Several experiments by using different methodologies have been performed to answer the stock market forecasting issues. A traditional linear model, like autoregressive integrated moving average (ARIMA) has been used, but the result is not satisfactory because it is not suitable for model financial series. Yet experts are likely observed another approach by using artificial neural networks. Artificial neural network (ANN) are found to be more effective in realizing the input-output mapping and could estimate any continuous function which given an arbitrarily desired accuracy. In details, in this paper will use maximal overlap discrete wavelet transform (MODWT) and graph theory to distinguish and determine between low and high frequencies, which in this case acted as fundamental and technical prediction of stock market trading. After processed dataset is formed, then we will advance to the next level of the training process to generate the final result that is the buy or sell signals given from information whether the stock price will go up or down.

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