An improved LDA approach

2004-2005 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

A face and palmprint recognition approach based on discriminant DCT feature extraction

2004-2005 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Development of Face Recognition on Raspberry Pi for Security Enhancement of Smart Home System

Nowadays, there is a growing interest in the smart home system using Internet of Things. One of the important aspect in the smart home system is the security capability which can simply lock and unlock the door or the gate. In this paper, we proposed a face recognition security system using Raspberry Pi which can be connected to the smart home system. Eigenface was used the feature extraction, while Principal Component Analysis (PCA) was used as the classifier. The output of face recognition algorithm is then connected to the relay circuit, in which it will lock or unlock the magnetic lock placed at the door. Results showed the effectiveness of our proposed system, in which we obtain around 90% face recognition accuracy. We also proposed a hierarchical image processing approach to reduce the training or testing time while improving the recognition accuracy

Diagnosis of Esophagitis Based on Face Recognition Techniques

Face recognition technology has evolved over years with the Principal Component Analysis (PCA) method being the benchmark for recognition efficiency. The face recognition techniques take care of variation of illumination, pose and other features of the face in the image. We envisage an application of these face recognition techniques for classification of medical images. The motivating factor being, given a condition of an organ it is represented by some typical features. In this paper we report the use of the face recognition techniques to classify the type of Esophagitis, a condition of inflammation of the esophagus. The image of the esophagus is captured in the process of endoscopy. We test PCA, Fisher Face method and Independent Component Analysis techniques to classify the images of the esophagus. Esophagitis is classified into four categories. The results of classification for each method are reported and the results are compared

Linear discriminant analysis for the small sample size problem: an overview

Dimensionality reduction is an important aspect in the pattern classification literature, and linear discriminant analysis (LDA) is one of the most widely studied dimensionality reduction technique. The application of variants of LDA technique for solving small sample size (SSS) problem can be found in many research areas e.g. face recognition, bioinformatics, text recognition, etc. The improvement of the performance of variants of LDA technique has great potential in various fields of research. In this paper, we present an overview of these methods. We covered the type, characteristics and taxonomy of these methods which can overcome SSS problem. We have also highlighted some important datasets and software/packages

SVM Based Approach for Multiface Detection and Recognition in Static Images

Recognizing and identifying a face from the real world, capture data that senses images is the demanding process in this advanced world. Because of varied face appearances, lighting effects and illumination of the background of the images, perceiving and recognizing multiple faces in a single image is a challenging process. This paper proposes a method that recognizes multiple faces in a single image using a different face recognition algorithm. Here, different approaches of face recognition using OpenCV and SVM algorithm have been compared and implemented for recognizing the multiple faces in a single image. In this method, the Haar Cascade Classifier, which is given by Viola Jones is used to detect the multiple faces in a single image. Local binary pattern histogram, eigenfaces and fisherfaces and Support Vector Machine learning algorithms are used to recognize multiple faces in a single image. These multiple face recognition algorithms are compared and tested over a different set of images