Face Recognition Using ANN With Reduce Feature by PCA in Wavelet Domain
نویسندگان
چکیده
Face recognition is an active research area in various streams such as pattern recognition, image processing. The Strong need of Face Recognition is personal identification and recognition without the cooperation of the participants. This paper presents face recognition using wavelet transform. A face recognition system follow these steps image decomposition, detection, feature extraction, and matching. For face detection haar wavelet is used to form the coefficient matrix and PCA is used for extracting features. These features are used to train the classifier based on artificial neural networks. The performance of the classifier is determined in terms of recognition rate for different training and testing data set. Keywords— Haar, WT, PCA , DWT, Neural Network etc.
منابع مشابه
Comparative Analysis of Wavelet-based Feature Extraction for Intramuscular EMG Signal Decomposition
Background: Electromyographic (EMG) signal decomposition is the process by which an EMG signal is decomposed into its constituent motor unit potential trains (MUPTs). A major step in EMG decomposition is feature extraction in which each detected motor unit potential (MUP) is represented by a feature vector. As with any other pattern recognition system, feature extraction has a significant impac...
متن کاملProposing an Enhanced Face Recognition Technique Using Multi- Wavelet Transform
Face recognition has been an active research for more than three decades. In general face recognition consists of feature extraction and classification. Principal Component Analysis (PCA) is one of the methods for features extraction under the appearance-based approach. However PCA came with large database, having some problems like load computation, and poor recognition accuracy. This research...
متن کاملAdaptive Principle Component Analysis to Improve Scale Invariant Feature Transform Matching for Face Recognition Applications
Image matching using feature extraction is an important issue in computer vision tasks. The main drawback of matching process is the bottleneck problem that rapidly appeared when the number of features increased. This paper produced an adaptive approach to improve Scale Invariant Feature Transform (SIFT) matching. The main idea is to increase the number of SIFT points by using Adaptive PCA in w...
متن کاملImproved Real-Time Face Recognition based on three Level wavelet Decomposition-Principal Component Analysis and Mahalanobis Distance
The development of research in the field of real-time face recognition is a study that is being developed in the last decade. Face recognition is used to identify person from an image or video. Recognition rate and computation time of real-time face recognition is one of the big challenges that must be developed. This study proposes a model of face recognition using the method of feature extrac...
متن کاملA Face Recognition Scheme Based On Principle Component Analysis and Wavelet Decomposition
In this paper, a new face recognition system based on Wavelet transform (HWT) and Principal Component Analysis (PCA) is presented. The image face is preprocessed and detected. The Haar wavelet is used to form the coefficient matrix for the detected face. The image feature vector is obtained by computing PCA for the coefficient matrix of DWT. A comparison between the proposed recognition system ...
متن کامل