A Gesture Recognition System Based on Time Domain Features and Linear Discriminant Analysis

Development of an optimal face recognition system will greatly depend on the characteristics of the selection process are as a basis to pattern recognition. In the characteristic selection process, there are 2 aspects that will be of mutual influence such the reduction of the amount of data used in the classification aspects and increasing discrimination ability aspects. Linear Discriminat Analysis method helps presenting the global structure while Laplacianfaces method is one method that is based on appearance (appearance-based method) in face recognition, in which the local manifold structure presented in the adjacency graph mapped from the training data points. Linear Discriminant Analysis QR decomposition has a computationally low cost because it has small dimensions so that the efficiency and scalability are very high when compared with algorithms of other Linear Discriminant Analysis methods. Laplacianfaces QR decomposition was a algorithm to obtain highly speed and accuracy, and tiny space to keep data on the face recognition. This algorithm consists of 2 stages. The first stage maximizes the distance of between-class scatter matrices by using QR decomposition and the second stage to minimize the distance of within-class scatter matrices. Therefore, it is obtained an optimal discriminant in the data. In this research, classification using the Euclidean distance method. In these experiments using face databases of the Olivetti-Att-ORL, Bern and Yale. The minimum error was achieved with the Laplacianfaces QR decomposition and Linear Discriminant Analysis QR decomposition are 5.88% and 9.08% respectively.

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A Novel Mechanism of Face Recognition Using Stepwise Linear Discriminant Analysis and Linear Vector Quantization Classifiers

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i7.96 ◽

2017 ◽

Vol 7 (7) ◽

pp. 48

Author(s):

Abdul Quyoom

Keyword(s):

Face Recognition ◽

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Vector Quantization ◽

Recognition System ◽

Face Image ◽

Linear Discriminant ◽

Linear Vector ◽

Face Features ◽

The Face

Face recognition is a hard and special case of computer vision and pattern recognition. It is a challenging problem due to various kinds of variations of face images. This paper proposes a robust face recognition system. Here stepwise linear discriminant analysis (SWLDA) is used for the feature extraction and Linear Vector Quantization (LVQ) Classifier is used for face recognition. The main focus of SWLDA is to select localized features from the face. In order to increase the low-between-class variance and to reduce within-class-variance among different expression classes and use F-test value through which results are analyzed. In recognition, firstly face is detected using canny edge detection method, after face detection SWLDA is employed to extract the face features, and end linear vector quantization is applied for face recognition. To achieve optimum results and increase the robustness of the proposed system, experiments are performed on various different samples of face image, which consist of face image with the different pose and facial expression in order to validate the system, we use two famous datasets which include Yale and ORL face database.

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Single Scale Retinex for Infant Pain Recognition

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.643.218 ◽

2014 ◽

Vol 643 ◽

pp. 218-223

Author(s):

Muhammad Naufal Mansor ◽

Ahmad Kadri Junoh ◽

Amran Ahmed ◽

Hussin Kamarudin ◽

Azrini Idris

Keyword(s):

Feature Extraction ◽

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Discrete Cosine Transform ◽

Recognition System ◽

Illumination Level ◽

Linear Discriminant ◽

Single Scale ◽

Infant Pain ◽

Pain Recognition

This paper discussed the crucial demand regarding the scheme to translate the silence voice from the newborn. The infant can’t afford to express their feeling of pain by voice. Hence, we proudly present an infant pain recognition system to overcome this matter. We employed the Single Scale Retinex (SSR) to remove the illumination level. Secondly, Discrete Cosine Transform (DCT) was adopted as the feature extraction. We determine the condition of the infants (pain/no pain) with Linear Discriminant Analysis (LDA). Several diagnosis tests were performed to estimate the performance of the suggested method under various illumination levels.

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3D hand gesture recognition based on Polar Rotation Feature and Linear Discriminant Analysis

2013 Fourth International Conference on Intelligent Control and Information Processing (ICICIP) ◽

10.1109/icicip.2013.6568070 ◽

2013 ◽

Cited By ~ 4

Author(s):

Yiding Wang ◽

Lin Zhang

Keyword(s):

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Gesture Recognition ◽

Hand Gesture Recognition ◽

Hand Gesture ◽

Linear Discriminant

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Zernike Moments Based Handwritten Pashto Character Recognition Using Linear Discriminant Analysis

Mehran University Research Journal of Engineering and Technology ◽

10.22581/muet1982.2101.14 ◽

2021 ◽

Vol 40 (1) ◽

pp. 152-159

Author(s):

Sardar Jehangir ◽

Sohail Khan ◽

Sulaiman Khan ◽

Shah Nazir ◽

Anwar Hussain

Keyword(s):

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Character Recognition ◽

Optical Character Recognition ◽

Recognition System ◽

Zernike Moments ◽

Linear Discriminant ◽

Individual Character ◽

The University ◽

Fold Cross Validation

This paper presents an efficient Optical Character Recognition (OCR) system for offline isolated Pashto characters recognition. Developing an OCR system for handwritten character recognition is a challenging task because of the handwritten characters vary both in shape and in style and most of the time the handwritten characters also vary among the individuals. The identification of the inscribed Pashto letters becomes even palling due to the unavailability of a standard handwritten Pashto characters database. For experimental and simulation purposes a handwritten Pashto characters database is developed by collecting handwritten samples from the students of the university on A4 sized page. These collected samples are then scanned, stemmed and preprocessed to form a medium sized database that encompasses 14784 handwritten Pashto character images (336 distinguishing handwritten samples for each 44 characters in Pashto script). Furthermore, the Zernike moments are considered as a feature extractor tool for the proposed OCR system to extract features of each individual character. Linear Discriminant Analysis (LDA) is followed as a recognition tool for the proposed recognition system based on the calculated features map using Zernike moments. Applicability of the proposed system is tested by validating it with 10-fold cross-validation method and an overall accuracy of 63.71% is obtained for the handwritten Pashto isolated characters using the proposed OCR system.

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