Machine learning approach for longitudinal face recognition of children

Kamble, Vaishali; Dale, Manisha

doi:10.1016/b978-0-323-85209-8.00011-0

“…The hyperplane is created according to the two criteria considered simultaneously: (1) maximizing the distance between the means of two classes and (2) minimizing the variation in each category. 32–34…”

Section: Resultsmentioning

confidence: 99%

“…The hyperplane is created according to the two criteria considered simultaneously: (1) maximizing the distance between the means of two classes and (2) minimizing the variation in each category. [32][33][34] Preprocessing of datasets, classication, and discrimination of samples were performed by the LDA method using the classication of each spectrum. According to the eqn (1)-( 4) and the data of the confusion matrix, the accuracy, precision, sensitivity and specicity of LDA were calculated:…”

Section: Multivariate Analysis Techniquementioning

confidence: 99%

Laser-induced breakdown spectroscopy combined with multivariate linear discriminant analysis for discriminating different growth phases of bacteria

Naseri,

Khalilzadeh,

Reza Darbani

et al. 2024

J. Anal. At. Spectrom.

2

0

View full text Add to dashboard Cite

show abstract

“…The softmax function is de ned as Eq. ( 4) (4) Where is softmax input vector, is a standard exponential function for input vector, is the number of classes in the multi-class classi er, is standard exponential function for output vector [33].…”

Section: Nose Projection (Np)mentioning

confidence: 99%

Designing an Expert System for Ranking Facial Attractiveness Based on Deep Learning and Fuzzy Inference System

Moridani

¹

,

Behbahani

²

,

Nejati

³

2023

Preprint

0

View full text Add to dashboard Cite

Objective: A wide range of social consequences is attributed to face preferences, from mate choices and decisions about social relationships to hiring decisions, including fashion modeling and marketing issues. This paper aims to design and develop an expert system based on the fuzzy inference system (FIS) for ranking facial attractiveness. Methods: Firstly, we reviewed the research on facial attractiveness and found that most papers do not use an expert system to rate the facial attractiveness of people. Therefore, this study used a deep learning method using a convolutional neural network (CNN) to recognize attractive faces. Then, with the help of the rules of the fuzzy inference system, an expert model was designed for ranking facial attractiveness. Results: We demonstrated that the combination of FIS and CNN is highly effective and excellent at ranking facial attractiveness. Our method performs better than other methods we have investigated in a small amount of data. The mean and standard deviation values of sensitivity, specificity, precision, and accuracy of the proposed model for detecting attractive and unattractive faces were 99.46±0.03%, 99.35±0.01%, 98.99±0.02%, and 99.7±0.01%, respectively. In addition, we have obtained a mean prediction accuracy for ranking facial attractiveness as 99.53+0.06%. Conclusion: We designed an expert ranking system for facial attractiveness to rank different faces according to facial structure features. As a result of our approach, we could also provide a deeper understanding of how FIS and CNN can recognize ranking facial attractiveness.

show abstract

“…Where σ is softmax z ⃗ input vector, e z i is a standard exponential function for input vector, k is the number of classes in the multi-class classifier, e z j is standard exponential function for output vector [33].…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Designing an Expert System for Ranking Facial Attractiveness Based on Deep Learning and Fuzzy Inference System

Moridani

¹

,

Behbahani

²

,

Nejati

³

2023

Preprint

0

View full text Add to dashboard Cite

Objective: A wide range of social consequences is attributed to face preferences, from mate choices and decisions about social relationships to hiring decisions, including fashion modeling and marketing issues. This paper aims to design and develop an expert system based on the fuzzy inference system (FIS) for ranking facial attractiveness. Methods: Firstly, we reviewed the research on facial attractiveness and found that most papers do not use an expert system to rate the facial attractiveness of people. Therefore, this study used a deep learning method using a convolutional neural network (CNN) to recognize attractive faces. Then, with the help of the rules of the fuzzy inference system, an expert model was designed for ranking facial attractiveness. Results: We demonstrated that the combination of FIS and CNN is highly effective and excellent at ranking facial attractiveness. Our method performs better than other methods we have investigated in a small amount of data. The mean and standard deviation values of sensitivity, specificity, precision, and accuracy of the proposed model for detecting attractive and unattractive faces were 99.46±0.03%, 99.35±0.01%, 98.99±0.02%, and 99.7±0.01%, respectively. In addition, we have obtained a mean prediction accuracy for ranking facial attractiveness as 99.53+0.06%. Conclusion: We designed an expert ranking system for facial attractiveness to rank different faces according to facial structure features. As a result of our approach, we could also provide a deeper understanding of how FIS and CNN can recognize ranking facial attractiveness.

show abstract

Machine learning approach for longitudinal face recognition of children

Cited by 15 publications

References 10 publications

Laser-induced breakdown spectroscopy combined with multivariate linear discriminant analysis for discriminating different growth phases of bacteria

Laser-induced breakdown spectroscopy combined with multivariate linear discriminant analysis for discriminating different growth phases of bacteria

Designing an Expert System for Ranking Facial Attractiveness Based on Deep Learning and Fuzzy Inference System

Designing an Expert System for Ranking Facial Attractiveness Based on Deep Learning and Fuzzy Inference System

Contact Info

Product

Resources

About