scholarly journals Diagnosis of Esophagitis Based on Face Recognition Techniques

2010 ◽  
Vol 4 (1) ◽  
pp. 58-62
Author(s):  
Santosh S Saraf ◽  
Gururaj R Udupi ◽  
Santosh D Hajare
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Analysis Techniques ◽  
The Face ◽  
Pca Method ◽  
Recognition Efficiency ◽  
Motivating Factor

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.

scholarly journals Patch-Based Principal Component Analysis for Face Recognition

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Tai-Xiang Jiang ◽  
Ting-Zhu Huang ◽  
Xi-Le Zhao ◽  
Tian-Hui Ma
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Nearest Neighbor ◽  
Spatial Information ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Face Images ◽  
Pca Method ◽  
Local Spatial Information

We have proposed a patch-based principal component analysis (PCA) method to deal with face recognition. Many PCA-based methods for face recognition utilize the correlation between pixels, columns, or rows. But the local spatial information is not utilized or not fully utilized in these methods. We believe that patches are more meaningful basic units for face recognition than pixels, columns, or rows, since faces are discerned by patches containing eyes and noses. To calculate the correlation between patches, face images are divided into patches and then these patches are converted to column vectors which would be combined into a new “image matrix.” By replacing the images with the new “image matrix” in the two-dimensional PCA framework, we directly calculate the correlation of the divided patches by computing the total scatter. By optimizing the total scatter of the projected samples, we obtain the projection matrix for feature extraction. Finally, we use the nearest neighbor classifier. Extensive experiments on the ORL and FERET face database are reported to illustrate the performance of the patch-based PCA. Our method promotes the accuracy compared to one-dimensional PCA, two-dimensional PCA, and two-directional two-dimensional PCA.

scholarly journals Deteksi Formalin Pada Buah Tomat (Lycopersicum esculentum Mill) Dengan Teknologi Hidung Elektronik (Electronic Nose)

2019 ◽  
Vol 4 (2) ◽  
pp. 359-366
Author(s):  
Irfan Maibriadi ◽  
Ratna Ratna ◽  
Agus Arip Munawar
Keyword(s):  
Principal Component Analysis ◽  
Electronic Nose ◽  
Principal Component ◽  
Component Analysis ◽  
Lycopersicum Esculentum ◽  
Olfactory Ability ◽  
Pca Method ◽  
The Difference ◽  
Electronic Nose Technology

Abstrak,  Tujuan dari penelitian ini adalah mendeteksi kandungan dan kadar formalin pada buah tomat dengan menggunakan instrument berbasis teknologi Electronic nose. Penelitian ini menggunakan buah tomat yang telah direndam dengan formalin dengan kadar 0.5%, 1%, 2%, 3%, 4%, dan buah tomat tanpa perendaman dengan formalin (0%). Jumlah sampel yang digunakan pada penelitian ini adalah sebanyak 18 sampel. Pengukuran spektrum beras menggunakan sensor Piezoelectric Tranducer. Klasifikasi data spektrum buah tomat menggunakan metode Principal Component Analysis (PCA) dengan pretreatment nya adalah Gap Reduction. Hasil penelitian ini diperoleh yaitu: Hidung elektronik mulai merespon aroma formalin pada buah tomat pada detik ke-8.14, dan dapat mengklasifikasikan kandungan dan kadar formalin pada buah tomat pada detik ke 25.77. Hidung elektronik yang dikombinasikan dengan metode principal component analysis (PCA) telah berhasil mendeteksikandungan dan kadar formalin pada buah tomat dengan tingkat keberhasilan sebesar 99% (PC-1 sebesar 93% dan PC-2 sebesar 6%). Perbedaan kadar formalin menjadi faktor utama yang menyebabkan Elektronik nose mampu membedakan sampel buah tomat yang diuji, karena semakin tinggi kadar formalin pada buah tomat maka aroma khas dari buah tomat pun semakin menghilang, sehingga Electronic nose yang berbasis kemampuan penciuman dapat membedakannya.Detect Formaldehyde on Tomato (Lycopersicum esculentum Mill) With Electronic Nose TechnologyAbstract, The purpose of this study is to detect the contents and levels of formalin in tomatoes by using instruments based on Electronic nose technology. This study used tomatoes that have been soaked in formalin with a concentration of 0.5%, 1%, 2%, 3%, 4%, 5% and tomatoes without soaking with formalin (0%). The samples in this study were 18 samples. The measurements of the intensity on tomatoes aroma were using Piezoelectric Transducer sensors. The classification of tomato spectrum data was using the Principal Component Analysis (PCA) method with Gap Reduction pretreatment. The results of this study were obtained: the Electronic nose began to respond the smell of formalin on tomatoes at 8.14 seconds, and it could classify the content and formalin levels in tomatoes at 25.77 seconds. Electronic nose combined with the principal component analysis (PCA) method have successfully detected the content and levels of formalin in tomatoes with a success rate at 99% (PC-1 of 93% and PC-2 of 6%). The difference of grade formalin levels is the main factor that causes Electronic nose to be able to distinguish the tomato samples tested, because the higher of formalin content in tomatoes, the distinctive of tomatoes aroma is increasingly disappearing. Thereby, the Electronic nose based on  the olfactory ability can distinguish them. 

scholarly journals Half-face based recognition using principal component analysis

2021 ◽  
Vol 22 (3) ◽  
pp. 1404
Author(s):  
Ahmed M. Alkababji ◽  
Sara Raed Abd
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition Method ◽  
Face Images ◽  
The Face ◽  
Considerable Problem ◽  
Full Face ◽  
Pca Algorithm

<span lang="EN-US">Face recognition is a considerable problem in the field of image processing. It is used daily in various applications from personal cameras to forensic investigations. Most of the provides solutions proposed based on full-face images, are slow to compute and need more storage. In this paper, we propose an effective way to reduce the features and size of the database in the face recognition method and thus we get an increase in the speed of discrimination by using half of the face. Taking advantage of face symmetry, the first step is to divide the face image into two halves, then the left half is processed using the principal component analysis (PCA) algorithm, and the results are compared by using Euclidian distance to distinguish the person. The system was trained and tested on ORL database. It was found that the accuracy of the system reached up to 96%, and the database was minimized by 46% and the running time was decreased from 120 msec to 70 msec with a 41.6% reduction.</span>

scholarly journals Combined PCA-Daugman Method : An Effcient Technique for Face and Iris Recognition

2020 ◽  
pp. 34-44
Author(s):  
Md. Mahbubul Alam ◽  
Md. Ashikur Rahman Khan ◽  
Zayed Us Salehin ◽  
Main Uddin ◽  
Sultana Jahan Soheli ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Iris Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Biometric System ◽  
Recognition Method ◽  
Person Identification ◽  
Multimodal Biometric System ◽  
Recognition Efficiency

Face and iris are very common individual bio-metric features for person identification. Face recognition is the method of identification a person uniquely using face. Principal component analysis is one of the algorithms for face recognition. Iris recognition in another method of person identification using iris. Very popular iris recognition method is Daugman algorithm. Unimodal biometric system has various difficulties to detect a person like noisy and unusual data. Multimodal biometric system combined more than one individual modalities like face and iris to increase the efficiency. In this work, we combined principal component analysis and Daugman algorithm with ORL, YALE, CASIA and Real face dataset to combine face and iris recognition to improve the recognition efficiency.

scholarly journals Deteksi Bubuk Kopi Luwak Murni dan Bubuk Kopi Luwak Campuran Dengan Teknologi Hidung Elektronik

2019 ◽  
Vol 4 (3) ◽  
pp. 105-114
Author(s):  
Mubarak Hulda ◽  
Fachruddin Fachruddin ◽  
Agus Arip Munawar
Keyword(s):  
Principal Component Analysis ◽  
Electronic Nose ◽  
Principal Component ◽  
Component Analysis ◽  
Powder Spectrum ◽  
Pca Method ◽  
Coffee Powder ◽  
Electronic Nose Technology ◽  
Spectrum Data

Abstrak. Kopi luwak merupakan kopi yang berasal dari hasil konsumsi hewan luwak (musang) yang  telah mengalami fermentasi di dalam pencernaan luwak selam 12 jam. Kopi luwak merupakan komoditi yang sangat diminati dan bernilai jual tinggi. Tujuan dari penelitian ini untuk membedakan bubuk kopi luwak murni dan bubuk kopi luwak campuran dengan memanfaatkan instrumen berbasis teknologi hidung elektronik (electronic nose). Penelitian ini menggunakan bubuk kopi luwak murni dan bubuk kopi arabika yang dicampurkan dengan perbandingan (50:50, 60:40. 70:30, 80:20 dan 90:10). Jumlah sampel yang digunakan pada penelitian ini adalah sebanyak 10 sampel. Pengukuran intensitas sinyal aroma bubuk kopi menggunakan sensor piezoelectric tranducers. Klasifikasi data spektrum bubuk kopi menggunakan metode Principal Component Analysis (PCA) dengan pretreatment nya adalah Gap Reduction. Hasil penelitian ini diperoleh yaitu: Hidung elektronik mulai merespon aroma bubuk kopi pada detik ke-5.64, dan dapat mengklasifikasikan bubuk kopi pada detik ke 11.09. Hidung elektronik yang dikombinasikan dengan metode principal component analysis (PCA) telah berhasil mendeteksi bubuk kopi luwak murni dan bubuk kopi luwak campuran dengan tingkat keberhasilan sebesar 100% (PC-1 sebesar 100% dan PC-2 sebesar 0%).Deteksi Murni Powder Kopi Luwak dan Campuran Kopi Luwak Bubuk Menggunakan Teknologi Hidung ElektronikAbstract. Civet coffee is coffee that comes from the consumption of civet animals (ferrets) that have undergone fermentation in the digestion of mongoose for 12 hours. Civet coffee is a commodity that is very popular and has a high selling value. The purpose of this study is to distinguish pure civet coffee powder and mixed civet coffee powder by using an instrument based on electronic nose technology. This study used pure civet coffee powder and arabica coffee powder mixed with comparisons (50:50, 60:40. 70:30, 80:20 and 90:10). The number of samples used in this study were 10 samples. The measurement of the intensity of coffee powder’s smell signals using piezoelectric tranducers. The classification of coffee powder spectrum data using the Principal Component Analysis (PCA) method with its pretreatment is Gap Reduction. The results of this study were obtained: The electronic nose starts responding to the smell of coffee powder at 5.85 seconds, and can classify coffee powder in 11.09 seconds. The electronic nose combined with the principal component analysis (PCA) method has succeeded in detecting pure civet coffee powder and mixed Civet coffee powder with a success rate of 100 % (PC-1 of 100% and PC-2 of 0%).     

Vision-Based Human Face Recognition Using Extended Principal Component Analysis

2013 ◽  
Vol 5 (4) ◽  
pp. 82-94 ◽  
Author(s):  
A. F. M. Saifuddin Saif ◽  
Anton Satria Prabuwono ◽  
Zainal Rasyid Mahayuddin ◽  
Teddy Mantoro
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Personal Identification ◽  
Human Face ◽  
Analysis Model ◽  
Human Face Recognition ◽  
Proposed Model ◽  
The Face

Face recognition has been used in various applications where personal identification is required. Other methods of person's identification and verification such as iris scan and finger print scan require high quality and costly equipment. The objective of this research is to present an extended principal component analysis model to recognize a person by comparing the characteristics of the face to those of new individuals for different dimension of face image. The main focus of this research is on frontal two dimensional images that are taken in a controlled environment i.e. the illumination and the background is constant. This research requires a normal camera giving a 2-D frontal image of the person that will be used for the process of the human face recognition. An Extended Principal Component Analysis (EPCA) technique has been used in the proposed model of face recognition. Based on the experimental results it is expected that proposed the EPCA performs well for different face images when a huge number of training images increases computation complexity in the database.

Analysis of age invariant face recognition using quadratic support vector machine-principal component analysis

2021 ◽  
pp. 1-15
Author(s):  
Ashutosh Dhamija ◽  
R. B. Dubey
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Face Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Identity Recognition ◽  
The Face ◽  
A New Technique ◽  
Age Range

Face recognition is one of the most challenging and demanding field, since aging affects the shape and structure of the face. Age invariant face recognition is a relatively new area in face recognition studies, which in real-world implementations recently gained considerable interest due to its huge potential and relevance. The Age invariant face recognition, however, is still evolving and evolving, providing substantial potential for further study and progress inaccuracy. Major issues with the age invariant face recognition involve major variations in appearance, texture, and facial features and discrepancies in position and illumination. These problems restrict the age invariant face recognition systems developed and intensify identity recognition tasks. To address this problem, a new technique Quadratic Support Vector Machine- Principal Component Analysis (QSVM-PCA) is introduced. Experimental results suggest that our QSVM-PCA achieved better results especially when the age range is larger than other existing techniques of face-aging dataset of FGNET. The maximum accuracy achieved by demonstrated methodology is 98.87%.

scholarly journals Principal Component Analysis (PCA) Method for Classification of Beef and Pork Aroma Based on Electronic Nose

2019 ◽  
Vol 1 (1) ◽  
pp. 5-8 ◽  
Author(s):  
Imam Tazi ◽  
Nur Laila Isnaini ◽  
Mutmainnah Mutmainnah ◽  
Avin Ainur
Keyword(s):  
Principal Component Analysis ◽  
Electronic Nose ◽  
Meat Products ◽  
Principal Component ◽  
Component Analysis ◽  
Cumulative Number ◽  
Organoleptic Evaluation ◽  
Pca Method ◽  
Meat Samples

There are several testing processes for consuming meat products. Organoleptic evaluation is an evaluation based on color, texture, smell, and taste. This research aims to find out the response pattern of 10 gas sensor array contained in the electronic nose against the odor pattern of beef and pork base on a smell. The classification method used is using the Principal Component Analysis (PCA) method. This method is expected to simplify the test of differences in beef and pork based on the aroma. The meat used is standard beef and pork consumption that has been sold in supermarkets. Samples of beef and pork are then ground until smooth. After that, it is weighed until it reaches 1 ounce. The meat samples were tested using an electronic nose consisting of 10 gas sensors. The multivariate analysis method was used to classify the aroma of beef and pork. The results of the data processing showed that the aroma classification of beef and pork which was indexed by the electronic nose was perfect. Based on the PCA method, the proportion of PC1 is 93.4%, and PC2 is 4.9%. From the second cumulative number, the value of the first PC was obtained 98.3%. This value indicates that only with 2-dimensional data, can represent ten dimensions of data. The loading plot shows that the MQ-138 and MQ-3 sensors are the most powerful sensors in testing samples of beef and pork.

scholarly journals Face Recognition Based on PCA Algorithm

2013 ◽  
pp. 79-83
Author(s):  
Taranpreet Singh Ruprah
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Face Recognition ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Principal Component ◽  
Histogram Equalization ◽  
Component Analysis ◽  
Back Propagation Neural Network ◽  
The Face

This paper is proposed the face recognition method using PCA with neural network back error propagation learning algorithm .In this paper a feature is extracted using principal component analysis and then classification by creation of back propagation neural network. We run our algorithm for face recognition application using principal component analysis, neural network and also calculate its performance by using the photometric normalization technique: Histogram Equalization and comparing with Euclidean Distance, and Normalized correlation classifiers. The system produces promising results for face verification and face recognition. Demonstrate the recognition accuracy for given number of input pattern.

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