Face Recognition System based on SURF and LDA Technique

Singh, Narpat; Kumar, Manoj B.; Bala, Manju C.

doi:10.5815/ijisa.2016.02.02

Cited by 8 publications

(5 citation statements)

References 13 publications

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“…The working principle of the LDA aims to determine an optimal projection for input data on spaces with very small dimensions. Therefore, LDA minimizes the spread of inputs in the same class and maximizes the spread of input data between different classes [21], the function of LDA can be seen in equation ( 3).…”

Section: Linear Discriminant Analysis (Lda)mentioning

confidence: 99%

Obstructive Sleep Apnea Detection using Frequency Analysis of Electrocardiographic RR Interval and Machine Learning Algorithms

2022

J Biomed Phys Eng

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Background: Obstructive Sleep Apnea (OSA) is a respiratory disorder due to obstructive upper airway (mainly in the oropharynx) periodically during sleep. The common examination used to diagnose sleep disorders is Polysomnography (PSG). Diagnose with PSG feels uncomfortable for the patient because the patient's body is fitted with many sensors.Objective: This study aims to propose an OSA detection using the Fast Fourier Transform (FFT) statistics of electrocardiographic RR Interval (R interval from one peak to the peak of the pulse of the next pulse R) and machine learning algorithms. Material and Methods:In this case-control study, data were taken from the Massachusetts Institute of Technology at Beth Israel Hospital (MIT-BIH) based on the Apnea ECG database (RR Interval). The machine learning algorithms were Linear Discriminant Analysis (LDA), Artificial Neural Network (ANN), K-Nearest Neighbors (K-NN), and Support Vector Machine (SVM). Results:The OSA detection technique was designed and tested, and five features of the FFT were examined, namely mean (f1), Shannon entropy (f2), standard deviation (f3), median (f4), and geometric mean (f5). The OSA detection found the highest performance using ANN. Among the ANN types tested, the ANN with gradient descent backpropagation resulted in the best performance with accuracy, sensitivity, and specificity of 84.64%, 94.21%, and 64.03%, respectively. The lowest performance was found when LDA was applied. Conclusion:ANN with gradient-descent backpropagation performed higher than LDA, SVM, and KNN for OSA detection.

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Section: Linear Discriminant Analysis (Lda)mentioning

confidence: 99%

Obstructive Sleep Apnea Detection using Frequency Analysis of Electrocardiographic RR Interval and Machine Learning Algorithms

2022

J Biomed Phys Eng

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“…The algorithm was tested on Far Infrared (FIR) database. Face recognition system based on speeded up robust features (SURF) and linear discriminant analysis (LDA) was introduced in [19]. DWT is applied on the dataset to produce a discrete output of nxn dimension matrix for input of size n. LDA is applied on this matrix for dimensionality reduction and sub-space mapping.…”

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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“…This algorithm is shown to have a higher recognition rate than the LBPH algorithm. Singh et al [16] introduced a facial landmark-based face recognition system. The analysis of the recognition involved determining the distance and slope between each face landmark.…”

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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Face Recognition System based on SURF and LDA Technique

Cited by 8 publications

References 13 publications

Obstructive Sleep Apnea Detection using Frequency Analysis of Electrocardiographic RR Interval and Machine Learning Algorithms

Obstructive Sleep Apnea Detection using Frequency Analysis of Electrocardiographic RR Interval and Machine Learning Algorithms

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

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