Face Recognition Using Fuzzy Moments Discriminant Analysis

In this work, an enhanced feature extraction method for holistic face recognition approach of gray intensity still image, namely Fuzzy Moment Discriminant Analysis is used. Which is first, based on Pseudo-Zernike Moments to extract dominant and significant features for each image of enrolled person, then the dimensionality of the moments features vectors is further reduced into discriminant moment features vectors using Linear Discriminant Analysis method, for these vectors the membership degrees in each class have been computed using Fuzzy K-Nearest Neighbor, after that, the membership degrees have been incorporated into the redefinition of the between-classes and within-classes scatter matrices to obtain final features vectors of  known persons. The test image is then compared with the faces enrollment images so that the face which has the minimum Euclidean distance with the test image is labeled with the identity of that image. Keyword: Zernike Moments, LDA, Fuzzy K-Nearest Neighbor

KLASIFIKASI TINGKAT KELANCARAN NASABAH DALAM MEMBAYAR PREMI DENGAN MENGGUNAKAN METODE K-NEAREST NEIGHBOR DAN ANALISIS DISKRIMINAN FISHER (Studi kasus: Data Nasabah PT. Prudential Life Samarinda Tahun 2019)

Classification is a technique to form a model of data that is already known to its classification group. The model that was formed will be used to classify new objects. The K-Nearest Neighbor (K-NN) algorithm is a method for classifying new objects based on their K nearest neighbor. Fisher discriminant analysis is a multivariate technique for separating objects in different groups to form a discriminant function for allocate new objects in groups. This research has a goal to determine the results of classifying customer premium payment status using the K-NN method and Fisher discriminant analysis and comparing the accuracy of the K-NN method classification and Fisher discriminant analysis on the insurance customer premium payment status. The data used is the insurance customer data of PT. Prudential Life Samarinda in 2019 with current premium payment status or non-current premium payment status and four independent variables are age, duration of premium payment, income and premium payment amount. The results of the comparative measurement of accuracy from the two analyzes show that the K-NN method has a higher level of accuracy than Fisher discriminant analysis for the classification of insurance customers premium payment status. The results of misclassification using the APER (Apparent Error Rate) in K-NN method is 15% while in Fisher discriminant analysis is 30%

PERBANDINGAN DAN ANALISIS K- NEAREST NEIGHBOR DAN LINEAR DISCRIMINANT ANALYSIS UNTUK KLASIFIKASI GENRE MUSIK

Seiring dengan perkembangan zaman, dalam kurun waktu yang relatif singkat musik berkembang dengan begitu cepat. Musik memiliki berbagai macam jenis genre antara lain: Metal, Blues, Pop, dan Dance. Genre musik adalah kategori dari karya seni, dalam hal ini khususnya musik, untuk mencirikan dan mengkategorikan musik yang kini tersedia dalam berbagai bentuk dan sumber. Pengklasifikasian genre musik secara otomatis dapat menjadi hal yang sangat membantu dalam pengembangan sistem temu-kembali untuk data audio. Pengolahan Sinyal Digital pada sinyal audio berkembang pesat untuk menghasilkan sebuah sistem yang bekerja secara digital. Sehingga diperlukan suatu pengembangan metode dan algoritma yang dapat mengklasifikasi genre secara tepat. Pada tugas akhir ini digunakan dua jenis metode yaitu metode K-Nearest Neighbor dan Linear Discriminant Analysis. Dimana pembentukan model klasifikasi K-Nearest Neighbor dan Linear Discriminant Analysis mengumpulkan ciri dari data acuan untuk menjadi data training saat pengujian. Proses klasifikasi genre sendiri dimulai dengan akuisisi data yaitu memilih file lagu yang akan di klasifikasikan kedalam genre file lagu tersebut. Selanjutnya dilakukan proses preprocessing, pengambilan ciri yang terdiri dari 12 nilai ciri, dan terakhir proses klasifikasi dengan membandingkan metode K-Nearest Neighbor dan Linear Discriminant Analysis untuk menghasilkan jenis genre dari file lagu yang dipilih dan dengan akurasi yang tertinggi. Pengujian yang dilakukan terhadap genre musik blues, dance, metal, dan pop menggunakan metode K-Nearest Neighbor dan dibandingkan dengan metode Linear Discriminant Analysis. skenario pengujian dilakukan dengan jumlah data latih 50 dan data uji 50 pada tiap genre, terhadap paramater Jenis dan Orde Filter dan didapat parameter terbaik yaitu Jenis filter Butterworth, Chebychev 1, Chebychev 2, dan Elliptic dengan orde 3,4 dan 5. Setelah dilakukan pengujian terhadap klasifikasi 4 genre musik di dapatkankan hasil sebesar 81,5% dengan K-Nearest Neighbor dan 85% dengan Linear Discriminant Analysis

Comparison of principal component analysis and linear discriminant analysis for face recognition (March 2007)

Abstract: In this paper two Face Recognition techniques, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), are considered and implemented using a Nearest Neighbor classifier. The performance of the two techniques is then compared in facial recognition and detection tasks. The comparisons are done using a facial recognition database captured for the project that contains images captured over a range of poses, lighting conditions and occlusions

Data adaptive kernal discriminant analysis using information complexity criterion and genetic algorithm

This dissertation proposes a new hybrid approach which is computationally effective and easy-to-use for selecting the best subset of predictor variables in discriminant analysis under the assumption that data sets do not follow the normal distribution. Our approach incorporates the information-theoretic measure of complexity (ICOMP) criterion with the genetic algorithm and kernel density estimators in discriminant analysis. This approach enables researchers to find both the optimal bandwidth matrix for the kernel density estimate and the best model from several competing models, which was a severe obstacle for researchers to apply kernel density estimate for discriminant analysis. The proposed approach is applied to four real data sets and compared with linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and k-Nearest Neighbor Discriminant Analysis (k-NNDA). Based on our application, we can conclude that our proposed approach performs better than LDA and QDA and performs as well as k-NNDA with respect to classification error rates. With our approach we can do all-possible-subset selection of variables for high-dimensional data to determine the best predictors discriminating between the groups

Face Identification by a Cascade of Rejection Classifiers

Nearest neighbor search is commonly employed in face recognition but it does not scale well to large dataset sizes. A strategy to combine rejection classifiers into a cascade for face identification is proposed in this paper. A rejection classifier for a pair of classes is defined to reject at least one of the classes with high confidence. These rejection classifiers are able to share discriminants in feature space and at the same time have high confidence in the rejection decision. In the face identification problem, it is possible that a pair of known individual faces are very dissimilar. It is very unlikely that both of them are close to an unknown face in the feature space. Hence, only one of them needs to be considered. Using a cascade structure of rejection classifiers, the scope of nearest neighbor search can be reduced significantly. Experiments on Face Recognition Grand Challenge (FRGC) version 1 data demonstrate that the proposed method achieves significant speed up and an accuracy comparable with the brute force Nearest Neighbor method. In addition, a graph cut based clustering technique is employed to demonstrate that the pairwise separability of these rejection classifiers is capable of semantic grouping.National Science Foundation (EIA-0202067, IIS-0329009); Office of Naval Research (N00014-03-1-0108

Facial Expression Recognition Based on Local Binary Patterns and Kernel Discriminant Isomap

Facial expression recognition is an interesting and challenging subject. Considering the nonlinear manifold structure of facial images, a new kernel-based manifold learning method, called kernel discriminant isometric mapping (KDIsomap), is proposed. KDIsomap aims to nonlinearly extract the discriminant information by maximizing the interclass scatter while minimizing the intraclass scatter in a reproducing kernel Hilbert space. KDIsomap is used to perform nonlinear dimensionality reduction on the extracted local binary patterns (LBP) facial features, and produce low-dimensional discrimimant embedded data representations with striking performance improvement on facial expression recognition tasks. The nearest neighbor classifier with the Euclidean metric is used for facial expression classification. Facial expression recognition experiments are performed on two popular facial expression databases, i.e., the JAFFE database and the Cohn-Kanade database. Experimental results indicate that KDIsomap obtains the best accuracy of 81.59% on the JAFFE database, and 94.88% on the Cohn-Kanade database. KDIsomap outperforms the other used methods such as principal component analysis (PCA), linear discriminant analysis (LDA), kernel principal component analysis (KPCA), kernel linear discriminant analysis (KLDA) as well as kernel isometric mapping (KIsomap)