Face recognition rate using different classifier methods based on PCA

Abbas, Eyad I.; Safi, Mohammed E.; Rijab, Khalida S.

doi:10.1109/crcsit.2017.7965559

Cited by 28 publications

(15 citation statements)

References 5 publications

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“…In order to evaluate the performance of ISVM-LEARN++ algorithm, experiments are conducted on three data sets from UCI repository (refer Table 1): Haberman's Survival, Blood Transfusion Service Center and Ionosphere. Performance is evaluated in terms of recognition rate and classification error rate [21].…”

Section: Experiments and Resultsmentioning

confidence: 99%

A Novel Incremental Learning Algorithm Based on Incremental Vector Support Machina and Incremental Neural Network Learn++

Chefrour¹,

Souici-Meslati²,

Difi³

et al. 2019

RIA

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Incremental learning refers to the learning of new information iteratively without having to fully retain the classifier. However, a single classifier cannot realize incremental learning if the classification problem is too complex and scalable. To solve the problem, this paper combines the incremental support vector machine (ISVM) and the incremental neural network Learn++ into a novel incremental learning algorithm called the ISVM-Learn++. The two incremental classifiers were merged by parallel combination and weighted sum combination. The proposed algorithm was tested on three datasets, namely, three databases Ionosphere, Haberman's Survival, and Blood Transfusion Service Center. The results show that the ISVM-Learn ++ achieved a learning rate of 98 %, better than that of traditional incremental learning algorithms. The research findings shed new light on incremental supervised machine learning.

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Section: Experiments and Resultsmentioning

confidence: 99%

A Novel Incremental Learning Algorithm Based on Incremental Vector Support Machina and Incremental Neural Network Learn++

Chefrour¹,

Souici-Meslati²,

Difi³

et al. 2019

RIA

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“…Mixture-distance and raw images have been found to reduce the error rate by up to 73.90% [48]. Abbas et al [1] also found that the cluster method with the squared Euclidean distance method produced a higher recognition rate than the Euclidean distance method, giving a human face recognition rate of 98%, compared with 95% when using the city-block distance method. This paper, in addition to localizing the facial features-a case similar to Sayeed et al [65], introduces a global (redundancy) facial domain or superset to guarantee search optimality.…”

Section: Svm Pattern Classificationmentioning

confidence: 99%

Feature redundancy approach to efficient face recognition in still images

2019

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This paper proposes the use of redundant features for efficient recognition of faces in still images using a novel system framework that offers a detailed systematic workflow for solving the facial recognition problem. It accepts still frontal face images, processes and represents salient facial features (face, eyes, nose, and mouth) using facial detection and extraction techniques. The extracted features are then modeled by an ensemble of self-organizing maps. The ensemble outputs are later reassembled into a single dataset consisting of a normalized image Euclidean distance matrix to enhance the search space for optimal convergence and classification. The feasibility of the framework is tested using an experiment facial database captured during the study, and three benchmark facial expression databases, namely the Extended Cohn-Kanade (CK+) database, the Japanese Female Facial Expressions database, and the MMI Facial Expression database. The results suggest that feature redundancy is indeed useful for efficient facial recognition, as the support vector machine classification recorded high accuracies across the various databases, with the normalized image Euclidean distance dataset producing the highest performance, when compared with the localized principal component analysis and unnormalized image Euclidean distance datasets. Furthermore, overall classification accuracy of above 99% was achieved for the experiment (nonexpressive still face) database, compared with the benchmark facial expression databases, which yielded slightly lower results. A future direction of this work is further improvement of the framework to robustly handle severe facial variations.

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“…The human face has a complex and dynamic structure (Delac et al, 2005), being the one of the most important information in biometric sciences based on personal identification (Abbas et al, 2017). The technique of analysis and understanding of images have gained prominence in recent years with successful in applications of facial recognition (Delac et al, 2005).…”

Section: Pcamentioning

confidence: 99%

“…The technique of analysis and understanding of images have gained prominence in recent years with successful in applications of facial recognition (Delac et al, 2005). The PCA algorithm allows the identification of patterns in the data maintain their identification status and effectively reducing the dimensions in human face images (Abbas et al, 2017). This reducing eliminates information irrelevant or redundant for to arrives in a higher compression ratio for the first component (Liu and Kau, 2017) producing a low-dimensional representation of the input data without significant loss of the original data.…”

Section: Pcamentioning

confidence: 99%

Novelty Detection Applied in Recognition of Facial Expressions

Bove

Cerqueira

Filho

2020

Anais Do Congresso Brasileiro De Automática 2020

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Esta pesquisa investiga a capacidade das redes Perceptron de Múltiplas Camadas (MLP) e Função de Base Radial (RBF) na tarefa de Detecção de Novidade (DN) no reconhecimento de expressões faciais usando recursos de vídeo. O conjunto de dados de vídeo utilizado é produzido por atores profissionais em estúdio com estados afetivos básicos do rosto humano. Os algoritmos Viola-Jones, Kanade-Lucas-Tomasi (KLT) e Análise de Componentes Principais (PCA) são usados na fase de pré-processamento para extrair atributos da face. Os resultados avaliam o desempenho das redes MLP e RBF na tarefa DN, usando novas expressões faciais compatíveis com as utilizadas na fase de treinamento e também examinam a capacidade das redes em DN usando as faces de atores nunca antes vistos pelas redes. Neste processo, as redes MLP e RBF têm uma precisão de 98% para tarefa de classificação, 68% e 98% para DN com dados semelhantes aos dados da fase de treinamento e 100% para DN com dados totalmente novos. Assim, este trabalho reúne métodos e técnicas aplicadas na DN utilizando Redes Neurais Artificiais (RNA), visando a produção de sistemas interativos de cognição no campo da computação afetiva, baseados em técnicas de Inteligência Artificial (IA) e Visão Computacional.

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Face recognition rate using different classifier methods based on PCA

Cited by 28 publications

References 5 publications

A Novel Incremental Learning Algorithm Based on Incremental Vector Support Machina and Incremental Neural Network Learn++

A Novel Incremental Learning Algorithm Based on Incremental Vector Support Machina and Incremental Neural Network Learn++

Feature redundancy approach to efficient face recognition in still images

Novelty Detection Applied in Recognition of Facial Expressions

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