Improving the performance of multi-class SVMs in face recognition with nearest neighbor rule

While current face recognition algorithms have provided convincing performance on frontal face poses, recognition is far less effective when the pose and illumination conditions vary. Here the authors show how compound image transforms can be used for face recognition in various poses and illumination conditions. The method works by first dividing each image into four equal-sized tiles. Then, image features are extracted from the face images, transforms of the images, and transforms of transforms of the images. Finally, each image feature is assigned with a Fisher score, and test images are classified by using a simple Weighted Nearest Neighbor rule such that the Fisher scores are used as weights. Experimental results using the full color FERET dataset show that with no parameter tuning, the accuracy of rank-10 recognition for frontal, quarter-profile, and half-profile images is ~98%, ~94% and ~91%, respectively. The proposed method also achieves perfect accuracy on several other face recognition datasets such as Yale B, ORL and JAFFE. An important feature of this method is that the recognition accuracy improves as the number of subjects in the dataset gets larger.

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Exploring the Uncertainty Space of Ensemble Classifiers in Face Recognition

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001415560029 ◽

2015 ◽

Vol 29 (03) ◽

pp. 1556002 ◽

Cited By ~ 7

Author(s):

Juan Luis Fernández-Martínez ◽

Ana Cernea

Keyword(s):

Face Recognition ◽

Nearest Neighbor ◽

Learning Algorithm ◽

Majority Voting ◽

Borda Count ◽

Discrete Wavelet ◽

Final Decision ◽

Ensemble Classifiers ◽

Classification Problems ◽

Uncertainty Space

In this paper, we present a supervised ensemble learning algorithm, called SCAV1, and its application to face recognition. This algorithm exploits the uncertainty space of the ensemble classifiers. Its design includes six different nearest-neighbor (NN) classifiers that are based on different and diverse image attributes: histogram, variogram, texture analysis, edges, bidimensional discrete wavelet transform and Zernike moments. In this approach each attribute, together with its corresponding type of the analysis (local or global), and the distance criterion (p-norm) induces a different individual NN classifier. The ensemble classifier SCAV1 depends on a set of parameters: the number of candidate images used by each individual method to perform the final classification and the individual weights given to each individual classifier. SCAV1 parameters are optimized/sampled using a supervised approach via the regressive particle swarm optimization algorithm (RR-PSO). The final classifier exploits the uncertainty space of SCAV1 and uses majority voting (Borda Count) as a final decision rule. We show the application of this algorithm to the ORL and PUT image databases, obtaining very high and stable accuracies (100% median accuracy and almost null interquartile range). In conclusion, exploring the uncertainty space of ensemble classifiers provides optimum results and seems to be the appropriate strategy to adopt for face recognition and other classification problems.

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The condensed nearest neighbor rule (Corresp.)

IEEE Transactions on Information Theory ◽

10.1109/tit.1968.1054155 ◽

1968 ◽

Vol 14 (3) ◽

pp. 515-516 ◽

Cited By ~ 1121

Author(s):

P. Hart

Keyword(s):

Nearest Neighbor ◽

Nearest Neighbor Rule

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Patch-Based Principal Component Analysis for Face Recognition

Computational Intelligence and Neuroscience ◽

10.1155/2017/5317850 ◽

2017 ◽

Vol 2017 ◽

pp. 1-9 ◽

Cited By ~ 10

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.

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Optimization of deep learning features for age-invariant face recognition

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v10i2.pp1833-1841 ◽

2020 ◽

Vol 10 (2) ◽

pp. 1833

Author(s):

Amal A. Moustafa ◽

Ahmed Elnakib ◽

Nihal F. F. Areed

Keyword(s):

Deep Learning ◽

Face Recognition ◽

Nearest Neighbor ◽

Recognition Rate ◽

Manhattan Distance ◽

Distance Metrics ◽

K Nearest Neighbor ◽

Face Images ◽

Knn Classifier ◽

Deep Learning Features

This paper presents a methodology for Age-Invariant Face Recognition (AIFR), based on the optimization of deep learning features. The proposed method extracts deep learning features using transfer deep learning, extracted from the unprocessed face images. To optimize the extracted features, a Genetic Algorithm (GA) procedure is designed in order to select the most relevant features to the problem of identifying a person based on his/her facial images over different ages. For classification, K-Nearest Neighbor (KNN) classifiers with different distance metrics are investigated, i.e., Correlation, Euclidian, Cosine, and Manhattan distance metrics. Experimental results using a Manhattan distance KNN classifier achieves the best Rank-1 recognition rate of 86.2% and 96% on the standard FGNET and MORPH datasets, respectively. Compared to the state-of-the-art methods, our proposed method needs no preprocessing stages. In addition, the experiments show its privilege over other related methods.

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Pengenalan Wajah Menggunakan Metode Linear Discriminant Analysis dan k Nearest Neighbor

Jurnal ULTIMATICS ◽

10.31937/ti.v9i1.557 ◽

2017 ◽

Vol 9 (1) ◽

pp. 1-9

Author(s):

Fandiansyah Fandiansyah ◽

Jayanti Yusmah Sari ◽

Ika Putri Ningrum

Keyword(s):

Feature Extraction ◽

Face Recognition ◽

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Nearest Neighbor ◽

Experimental Result ◽

K Nearest Neighbor ◽

Linear Discriminant ◽

Nearest Neighbor Classifier ◽

Neighbor Classifier

Face recognition is one of the biometric system that mostly used for individual recognition in the absent machine or access control. This is because the face is the most visible part of human anatomy and serves as the first distinguishing factor of a human being. Feature extraction and classification are the key to face recognition, as they are to any pattern classification task. In this paper, we describe a face recognition method based on Linear Discriminant Analysis (LDA) and k-Nearest Neighbor classifier. LDA used for feature extraction, which directly extracts the proper features from image matrices with the objective of maximizing between-class variations and minimizing within-class variations. The features of a testing image will be compared to the features of database image using K-Nearest Neighbor classifier. The experiments in this paper are performed by using using 66 face images of 22 different people. The experimental result shows that the recognition accuracy is up to 98.33%. Index Terms—face recognition, k nearest neighbor, linear discriminant analysis.

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Using Partial Differential Equation Face Recognition Model to Evaluate Students’ Attention in a College Chinese Classroom

Advances in Mathematical Physics ◽

10.1155/2021/3950445 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Xia Miao ◽

Ziyao Yu ◽

Ming Liu

Keyword(s):

Differential Equation ◽

Partial Differential Equation ◽

Face Recognition ◽

Sparse Representation ◽

Nearest Neighbor ◽

Learning Model ◽

Local Geometry ◽

Partial Differential ◽

The Face ◽

Expression Score

The partial differential equation learning model is applied to another high-level visual-processing problem: face recognition. A novel feature selection method based on partial differential equation learning model is proposed. The extracted features are invariant to rotation and translation and more robust to illumination changes. In the evaluation of students’ concentration in class, this paper firstly uses the face detection algorithm in face recognition technology to detect the face and intercept the expression data, and calculates the rise rate. Then, the improved model of concentration analysis and evaluation of a college Chinese class is used to recognize facial expression, and the corresponding weight is given to calculate the expression score. Finally, the head-up rate calculated at the same time is multiplied by the expression score as the final concentration score. Through the experiment and analysis of the experimental results in the actual classroom, the corresponding conclusions are drawn and teaching suggestions are provided for teachers. For each face, a large neighborhood set is firstly selected by the k -nearest neighbor method, and then, the sparse representation of sample points in the neighborhood is obtained, which effectively combines the locality of k -nearest neighbor and the robustness of sparse representation. In the sparse preserving nonnegative block alignment algorithm, a discriminant partial optimization model is constructed by using sparse reconstruction coefficients to describe local geometry and weighted distance to describe class separability. The two algorithms obtain good clustering and recognition results in various cases of real and simulated occlusion, which shows the effectiveness and robustness of the algorithm. In order to verify the reliability of the model, this paper verified the model through in-class practice tests, teachers’ questions, and interviews with students and teachers. The results show that the proposed joint evaluation method based on expression and head-up rate has high accuracy and reliability.

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