scholarly journals Contradictory of the Laplacian Smoothing Transform and Linear Discriminant Analysis Modeling to Extract the Face Image Features

2017 ◽  
Vol 15 (4) ◽  
pp. 1794
Author(s):  
Arif Muntasa ◽  
Indah Agustien Siradjuddin
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Image Features ◽  
Face Image ◽  
Linear Discriminant ◽  
The Face ◽  
Laplacian Smoothing

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

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.

scholarly journals Texture Characteristic of Local Binary Pattern on Face Recognition with Probabilistic Linear Discriminant Analysis

2020 ◽  
Vol 1 (1) ◽  
pp. 22-26
Author(s):  
Isnawati Muslihah ◽  
Muqorobin Muqorobin
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Local Binary Pattern ◽  
Face Image ◽  
Image Texture ◽  
Identification System ◽  
Linear Discriminant ◽  
Face Images ◽  
The Face

Face recognition is an identification system that uses the characteristics of a person's face for processing. There is a feature in the face image so that it can be distinguished between one face and another face. One way to recognize face images is to analyze the texture of the face image. Texture analysis generally requires a feature extraction process. In different images, the characteristics will also differ. This characteristic will be the basis for the recognition of facial images. However, existing face recognition methods experience efficiency problems and rely heavily on the extraction of the right features. This study aims to study the texture characteristics of the extraction results using the Local Binary Pattern (LBP) method which is applied to deal with the introduction of Probabilistic Linear Discriminant Analysis (PLDA). The data used in this study are human face images from the AR Faces database, consisting of 136 objects (76 men and 60 women), each of which has 7 types of images Based on the results of testing shows the LBP method can produce the highest accuracy with a value of 95.53% in the introduction of PLDA.

scholarly journals APLIKASI PRESENSI PENGENALAN WAJAH DENGAN MENGGUNAKAN ALGORITMA HAAR CASCADE CLASSIFIER

Telematika ◽  
2020 ◽  
Vol 16 (2) ◽  
pp. 87
Author(s):  
Moh. Wahyu Septyanto ◽  
Herry Sofyan ◽  
Herlina Jayadianti ◽  
Oliver Samuel Simanjuntak ◽  
Dessyanto Boedi Prasetyo
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Square Function ◽  
Cascade Classifier ◽  
Linear Discriminant ◽  
Long Time ◽  
The Face ◽  
Total Failure ◽  
Haar Cascade ◽  
Employee Attendance

AbstractPresence using face already widely adopted as a way of monitoring employee attendance. Research on using facial Presence never been done before by applying algorithms and algorithms Eigenface linear discriminant analysis (LDA). However, previous research has found that there are still weaknesses in the algorithms used. The weakness is that the process of identifying which takes a long time because the process of calculating the value carried on the overall image or image and the distance of the face of the webcam can affect the process of identifying faces. In this study, the algorithm used is haar cascade classifier algorithm. Haar classifier cascade or known by other names haar-like features are the rectangular features (square function), which gives an indication of the specifics on a picture or image. Principle Haar-like features are recognizing objects based on simple values of the features but not the pixel values of the object image. This method has the advantage that the computation is very fast, because it depends on the number of pixels in a square instead of each pixel value of an image. Haar classifier cascade also still be able to identify faces even if the distance face with the webcam is considerably due to the value of the facial features can still be identified. Results from this study that the system can identify the face with a good degree of accuracy. Tests carried out to 13 employees Starcross Store with each employee doing 30 times the experiment presence. Attendance successful has the success rate is 87% and 13% of the total failure of the experiment 390 times. Some absences failed to happen because there are several factors that can affect attendance as high luminance, uplifted head position, and the use of attributes (hats, glasses, etc.).Keywords : Presence, face recognition, Haar cascade classifier algorithmPresensi menggunakan wajah sudah banyak diterapkan sebagai cara untuk pemantauan kehadiran pegawai. Penelitian tentang presensi menggunakan wajah pernah dilakukan sebelumnya dengan menerapkan algoritma eigenface dan algoritma linear discriminant analysis (LDA). Namun dari penelitian sebelumnya telah ditemukan kelemahan yaitu pada proses pengidentifikasian yang membutuhkan waktu cukup lama dikarenakan proses perhitungan nilai dilakukan pada keseluruhan citra atau image dan jauhnya jarak wajah dari webcam dapat mempengaruhi proses pengidentifikasian wajah tersebut. Pada penelitian ini algoritma yang digunakan adalah algoritma haar cascade classifier. Haar cascade classifier atau yang dikenal dengan nama lain haar-like features merupakan rectangular features (fungsi persegi), yang memberikan indikasi secara spesifik pada sebuah gambar atau image. Prinsip Haar-like features adalah mengenali obyek berdasarkan nilai sederhana dari fitur tetapi bukan merupakan nilai piksel dari image obyek tersebut. Metode ini memiliki kelebihan yaitu komputasinya sangat cepat, karena hanya bergantung pada jumlah piksel dalam persegi bukan setiap nilai piksel dari sebuah image. Haar cascade classifier juga masih dapat mengidentifikasi wajah walaupun jarak wajah dengan webcam terbilang jauh dikarenakan nilai fitur wajah masih dapat diidentifikasi. Hasil dari penelitian ini bahwa sistem dapat mengidentifikasi wajah dengan tingkat akurasi baik. Pengujian dilakukan kepada 13 karyawan Starcross Store dengan masing-masing karyawan melakukan 30 kali percobaan presensi. Absensi yang berhasil memiliki nilai keberhasilan 87% dan 13% gagal dari total percobaan 390 kali. Beberapa absensi yang gagal terjadi karena ada beberapa faktor yang dapat mempengaruhi absensi seperti pencahayaan yang tinggi, posisi kepala yang mendongkak dan penggunaan atribut (topi, kacamata, dsb).Kata Kunci : Presensi, Pengenalan Wajah, Algoritma Haar Cascade Classifier 

Effect of Topography on Maize Grains Elemental Profile: A Chemometric Approach

2020 ◽  
Vol 16 (8) ◽  
pp. 1079-1087
Author(s):  
Jorgelina Z. Heredia ◽  
Carlos A. Moldes ◽  
Raúl A. Gil ◽  
José M. Camiña
Keyword(s):  
Cluster Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Microwave Plasma ◽  
Emission Spectrometry ◽  
Linear Discriminant ◽  
Maize Seeds ◽  
Elemental Profile ◽  
Mineral Profile ◽  
Topographic Characteristics

Background: The elemental composition of maize grains depends on the soil, land and environment characteristics where the crop grows. These effects are important to evaluate the availability of nutrients with complex dynamics, such as the concentration of macro and micronutrients in soils, which can vary according to different topographies. There is available scarce information about the influence of topographic characteristics (upland and lowland) where culture is developed with the mineral composition of crop products, in the present case, maize seeds. On the other hand, the study of the topographic effect on crops using multivariate analysis tools has not been reported. Objective: This paper assesses the effect of topographic conditions on plants, analyzing the mineral profiles in maize seeds obtained in two land conditions: uplands and lowlands. Materials and Methods: The mineral profile was studied by microwave plasma atomic emission spectrometry. Samples were collected from lowlands and uplands of cultivable lands of the north-east of La Pampa province, Argentina. Results: Differentiation of maize seeds collected from both topographical areas was achieved by principal components analysis (PCA), cluster analysis (CA) and linear discriminant analysis (LDA). PCA model based on mineral profile allowed to differentiate seeds from upland and lowlands by the influence of Cr and Mg variables. A significant accumulation of Cr and Mg in seeds from lowlands was observed. Cluster analysis confirmed such grouping but also, linear discriminant analysis achieved a correct classification of both the crops, showing the effect of topography on elemental profile. Conclusions: Multi-elemental analysis combined with chemometric tools proved useful to assess the effect of topographic characteristics on crops.

Colorectal Cancer Classification and Survival Analysis Based on an Integrated RNA and DNA molecular signature

2020 ◽  
Vol 15 ◽  
Author(s):  
Mohanad Mohammed ◽  
Henry Mwambi ◽  
Bernard Omolo
Keyword(s):  
Colorectal Cancer ◽  
Survival Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Negative Binomial ◽  
Sub Saharan Africa ◽  
Support Vector ◽  
Mutation Status ◽  
Linear Discriminant ◽  
Rnaseq Data

Background: Colorectal cancer (CRC) is the third most common cancer among women and men in the USA, and recent studies have shown an increasing incidence in less developed regions, including Sub-Saharan Africa (SSA). We developed a hybrid (DNA mutation and RNA expression) signature and assessed its predictive properties for the mutation status and survival of CRC patients. Methods: Publicly-available microarray and RNASeq data from 54 matched formalin-fixed paraffin-embedded (FFPE) samples from the Affymetrix GeneChip and RNASeq platforms, were used to obtain differentially expressed genes between mutant and wild-type samples. We applied the support-vector machines, artificial neural networks, random forests, k-nearest neighbor, naïve Bayes, negative binomial linear discriminant analysis, and the Poisson linear discriminant analysis algorithms for classification. Cox proportional hazards model was used for survival analysis. Results: Compared to the genelist from each of the individual platforms, the hybrid genelist had the highest accuracy, sensitivity, specificity, and AUC for mutation status, across all the classifiers and is prognostic for survival in patients with CRC. NBLDA method was the best performer on the RNASeq data while the SVM method was the most suitable classifier for CRC across the two data types. Nine genes were found to be predictive of survival. Conclusion: This signature could be useful in clinical practice, especially for colorectal cancer diagnosis and therapy. Future studies should determine the effectiveness of integration in cancer survival analysis and the application on unbalanced data, where the classes are of different sizes, as well as on data with multiple classes.

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