Face Recognition Using DCT and Hierarchical RBF Model

Chen, Yuehui; Zhao, Yaou

doi:10.1007/11875581_43

Cited by 14 publications

(5 citation statements)

References 11 publications

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“…ANFIS is a five-layered structure consisting of the fuzzy layer, product layer, normalized layer, de-fuzzy layer, and total output layer [54] (see Figure 4). In addition to ANFIS, we also utilized a Radial Basis Function (RBF) network, which is a feed-forward neural network with one hidden layer of RBF units and a linear output layer [55,56]. The output of the RBF network is calculated as follows [57]:…”

Section: Fundamentals and Theoriesmentioning

confidence: 99%

Unlocking the Potential of Soft Computing for Predicting Lubricant Elemental Spectroscopy

Pourramezan,

Rohani,

Abbaspour-Fard

2023

Lubricants

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Predictive maintenance of mechanical systems relies on accurate condition monitoring of lubricants. This study assesses the performance of soft computing models in predicting the elemental spectroscopy (Fe, Pb, Cu, Cr, Al, Si, and Zn) of engine lubricants, based on the electrical properties (ε′, ε″, and tan δ) of oil samples. The study employed a dataset of 49 lubricant samples, comprising elemental spectroscopy and dielectric properties, to train and test several soft computing models (RBF, ANFIS, SVM, MLP, and GPR). Performance of the models was evaluated using error metrics such as MAPE, RMSE, and EF. The RBF model delivered the most accurate predictions for silicon at 7.4 GHz, with an RMSE of 0.4 and MAPE of 0.7. Performance was further improved by fine-tuning RBF parameters, such as the hidden size and training algorithm. The sensitivity analysis showed that utilizing all three input electrical properties (ε′, ε″, and tan δ) resulted in the lowest errors. Nevertheless, there are limitations to the study. In our country, measuring the electrical properties of engine lubricants and equipment is not a common practice, which leads to a limited number of samples studied. Despite these limitations, this study offers a proof-of-concept for predicting lubricant conditions based on readily measurable electrical properties. This paves the way for developing machine learning-based real-time lubricant monitoring systems.

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Section: Fundamentals and Theoriesmentioning

confidence: 99%

Unlocking the Potential of Soft Computing for Predicting Lubricant Elemental Spectroscopy

Pourramezan,

Rohani,

Abbaspour-Fard

2023

Lubricants

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“…RBF network acts as a feed-forward neural network with one hidden layer of RBF units and a linear output layer [29,30]. The output is given as [31]:…”

Section: Extracted Datasetsmentioning

confidence: 99%

Comparative Evaluation of the Models for Predicting the Rela-Tionship Between Electrical and Chemical Properties of Engine Lubricants: An Exact Monitoring Approach

Pourramezan¹,

Rohani²,

Abbaspour‐Fard³

2023

Preprint

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Condition monitoring is a primary principle for predictive maintenance. In this regard, several techniques have been employed, each of which has its strengths and weaknesses. In this work, the relationships between the electrical (ε', ε", tan δ) and chemical (Fe, Pb, Cu, Cr, Al, Si, Zn) properties of engine lubricants were investigated using soft computing models. The models' performance was evaluated considering some criteria, including RMSE and MAPE. The RBF model was determined as the best one to predict chemical properties (Fe, Pb, Cu, Cr, Al, Si, Zn) through electrical indexes (ε', ε", tan δ). The RBF’s parameters (e.g., hidden size and training algorithm) were then optimized for optimum performance. For instance, in the RBF model implemented to predict Al, the best hidden size and training algorithmic were 15 and 'trainlm', respectively. Finally, by sensitivity analysis it was observed that eliminating any of the inputs does not improve the performance of the RBF model performance. In general, the results of the current research showed that the electrical properties (e.g., ε', ε", tan δ) have the capability of estimating the contaminants in the lubricants which can improve the existing monitoring methods.

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“…Whereas the secondary features for the purpose of tracking where retrieved from H-and S-channels of HSV colorspace, the secondary features for the purpose of recognition are calculated with the use of V-channel. We selected the DCT coding mainly due to its ease of application, known successful applications to face recognition [20], [21], and the potential of introducing identity recognition mechanisms into the existing compression schemes, which already utilize the DCT commonly.…”

Section: Secondary Face Features For Recognitionmentioning

confidence: 99%

Face Tracking and Recognition with the Use of Particle-Filtered Local Features

Stasiak,

Pacut

2023

JTIT

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A consistent particle filtering-based framework for the purpose of parallel face tracking and recognition from video sequences is proposed. A novel approach to defining randomized, particle filtering-driven local face features for the purpose of recognition is proposed. The potential of cumulating classification decisions based on the proposed feature set definition is evaluated. By applying cumulation mechanisms to the classification results determined from single frames and with the use of particle-filtered features, good recognition rates are obtained at the minimal computational cost. The proposed framework can operate in real-time on a typical modern PC. Additionally, the application of cumulation mechanisms makes the framework resistant to brief visual distortions, such as occlusions, head rotations or face expressions. A high performance is also obtained on low resolution images (video frames). Since the framework is based on the particle filtering principle, it is easily tunable to various application requirements (security level, hardware constraints).

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Face Recognition Using DCT and Hierarchical RBF Model

Cited by 14 publications

References 11 publications

Unlocking the Potential of Soft Computing for Predicting Lubricant Elemental Spectroscopy

Unlocking the Potential of Soft Computing for Predicting Lubricant Elemental Spectroscopy

Comparative Evaluation of the Models for Predicting the Rela-Tionship Between Electrical and Chemical Properties of Engine Lubricants: An Exact Monitoring Approach

Face Tracking and Recognition with the Use of Particle-Filtered Local Features

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