Noisy faces recognition based on PCNN and PCA

Sun, Fengjun; He, Min; Nie, Rencan; Zhangyong, Wu

doi:10.1109/icemi.2017.8265797

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…Higher accuracy was achieved with the small number of face images. In [17] have proposed a technique for faces recognition in which Pulse Coupled Neural Networks (PCNN) was employed to effectively suppress the noise and cluster the characteristic region of noisy faces, the PCA was used for dimensionality reduction and feature extraction. SVM is used for classification and recognition.…”

Section: Literature Surveymentioning

confidence: 99%

Performance Evaluation of Face Recognition system by Concatenation of Spatial and Transformation Domain Features

K¹,

Ravi²

2021

IJCNIS

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Face biometric system is one of the successful applications of image processing. Person recognition using face is the challenging task since it involves identifying the 3D object from 2D object. The feature extraction plays a very important role in face recognition. Extraction of features both in spatial as well as frequency domain has more advantages than the features obtained from single domain alone. The proposed work achieves spatial domain feature extraction using Asymmetric Region Local Binary Pattern (ARLBP) and frequency domain feature extraction using Fast Discrete Curvelet Transform (FDCT). The obtained features are fused by concatenation and compared with trained set of features using different distance metrics and Support Vector Machine (SVM) classifier. The experiment is conducted for different face databases. It is shown that the proposed work yields 95.48% accuracy for FERET, 92.18% for L-space k, 76.55% for JAFFE and 81.44% for NIR database using SVM classifier. The results show that the proposed system provides better recognition rate for SVM classifier when compare to the other distance matrices. Further, the work is also compared with existing work for performance evaluation.

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Section: Literature Surveymentioning

confidence: 99%

Performance Evaluation of Face Recognition system by Concatenation of Spatial and Transformation Domain Features

K¹,

Ravi²

2021

IJCNIS

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“…Feifei et al [2] suggested a method for recognizing faces that used PCA for feature extraction and dimensionality reduction, and Pulse Coupled Neural Networks (PCNN) to efficiently suppress noise and cluster the distinctive area of noisy faces. SVM is used for classification and recognition.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Some Selected Classification Algorithms in a Facial Recognition System

Adio,

Ogunwuyi,

Oyediran

et al. 2024

AJERD

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Facial Recognition (FR) has been an active area of research and has diverse applicable environment, it continues to be a challenging research topic. With the development of image processing and pattern recognition technology, there are many challenges in machine learning to select the appropriate classification algorithms, most especially in the area of classification of extracted features to have low classification time, high sensitivity and accuracy of the classification algorithms, so it is very important to explore the performance of different algorithms in image classification. The three selected supervised learning classification algorithms: Learning Vector Quantization (LVQ), Relevance Vector Machine (RVM), and Support Vector Machine (SVM) performance were evaluated so as to know the most effective out of the selected algorithms for facial images classification. The development of the system has four stages, the first stage is image acquisition and 180 images were taken by digital camera under same illumination and light colour background. The second stage is pre-processing to improve the images data by suppressing unwilling distortion; grayscale and normalization were used for image pre-processing. The third stage is feature extraction; Discrete Cosine Transform (DCT) is adopted for this purpose. While the fourth stage is face recognition classification, Receiver Operating Characteristics (ROC) was used to test the performance of each the three algorithms. However the Learning Vector Quantization algorithm, Relevance Vector Machine and Support Vector Machine performance have not been compared together to the most effective out of the three algorithms in term of False Positive Rate, Sensitivity, Specificity, Precision, Accuracy and Computation Time. Hence, this work evaluated the performance of the Learning Vector Quantization; Relevance Vector Machine and Support Vector Machine classification algorithms in facial recognition system and Support Vector Machine outwit the other two algorithms in facial recognition in term of specificity, recognition time and recognition accuracy at different threshold.

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An Extensive Study on Traditional-to-Recent Transformation on Face Recognition System

Juneja

Rana

2021

Wireless Pers Commun

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Noisy faces recognition based on PCNN and PCA

Cited by 5 publications

References 4 publications

Performance Evaluation of Face Recognition system by Concatenation of Spatial and Transformation Domain Features

Performance Evaluation of Face Recognition system by Concatenation of Spatial and Transformation Domain Features

Performance Evaluation of Some Selected Classification Algorithms in a Facial Recognition System

An Extensive Study on Traditional-to-Recent Transformation on Face Recognition System

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