Optimal feature selection with hybrid classification for automatic face shape classification using fitness sorted Grey wolf update

Sukumaran, Asha; Brindha, Thomas

doi:10.1007/s11042-021-10710-9

Cited by 4 publications

(3 citation statements)

References 38 publications

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“…Afterward, the eight markers were repeatedly trailed via LucasKanade optical flow algorithm while subjects' coherent facial expressions. In [37] [38], used integration of methods in feature extraction and classification to obtain accurate results for classifying face shape and race. In [37] a model that used ASM-AAM for feature extraction and a combination of classifiers such as CNN and NN for classification was built.…”

Section: Reviews On Classification Algorithmsmentioning

confidence: 99%

“…In [37] [38], used integration of methods in feature extraction and classification to obtain accurate results for classifying face shape and race. In [37] a model that used ASM-AAM for feature extraction and a combination of classifiers such as CNN and NN for classification was built. An optimization technique was incorporated in feature selection and weight optimization in the classifier using a Fitness Sorted Grey Wolf Update (FS-GU) algorithm.…”

Section: Reviews On Classification Algorithmsmentioning

confidence: 99%

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A Review of Methodologies in Machine Learning For Face Shape Analysis

Sukumaran

2023

IJRASET

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Face finding and face analysis have been employed as the basis of many researchers in recent years. The outline of an object gives the structure and look of an object. In applications like face recognition, a form of the face acts as a significant role. This paper reviews different methods adopted in different works to frame the shape of the human face that would be useful for many other future works. A list of existing machine learning and deep learning algorithms for face shape analysis is discussed and reviewed. It also discusses the result and accuracy of different methods that act as intermediate steps in achieving the shape of a human face and also a number of suggestions for achieving optimization in the routine of the work. A review of various performance measures such as time taken, error rate, classification rate, and memory are discussed.

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Section: Reviews On Classification Algorithmsmentioning

confidence: 99%

Section: Reviews On Classification Algorithmsmentioning

confidence: 99%

A Review of Methodologies in Machine Learning For Face Shape Analysis

Sukumaran

2023

IJRASET

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“…Moreover, different tools can be used to select the feature such as discriminant analysis (DA), principal component analyses (PA), decision tree (T), and multilayer perceptron (MLP). Also, feature selection found many applications worldwide to solve real-world problems such as landslide prediction [14,15], medical application [16,17], food industries [18,19], face recognitions [20,21], and many other applications [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Software Defect Prediction through Neural Network and Feature Selections

Alkhasawneh

2022

Applied Computational Intelligence and Soft Computing

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Software failure such as software defect causes billion of dollar loss every year. Software failure also affects billion of people worldwide. Inadequate software testing can cause software failure. To predict the software defect, this study proposed a model consisting of feature selection and classifications. The correlation base method was used for feature selection, and radial base function neural network (RBF) was used for classification. Also, for testing the proposed system, fourteen NASA data sets were used including CM1, JM1, KC1, KC2, KC3, KC4, MC1, MC2, MW1, PC1, PC2, PC3, PC4, and PC5. The data set was divided using the well-known K-cross-validation methods which were performed to divide the data set for training and testing the RBF. The RBF were trained and tested before and after feature selections. Precision, recall, F-measure, and accuracy are four methods used to evaluate the performance of the proposed methods. The precision obtained for the fourteen data sets was CM1, 94.01%; JM1, 85.18%; KC1, 83.24%; KC2, 81.27%; KC3, 79.30%; KC4, 85.29%; MC1, 99.89%; MC2, 73.27%; MW1, 90.90%; PC1, 98.79%; PC2, 100%; PC3, 95.67%; PC4, 95.12%; and PC5, 80.89%. Recall was as follows: CM1, 95.78%; JM1, 87.89%; KC1, 86.24%; KC2, 83.82%; KC3, 82.10%; KC4, 86.28%; MC1, 100%; MC2, 76.67%; MW1, 92.09%; PC1, 99.98%; PC2, 100%; PC3, 96.23%; PC4, 95.17%; and PC5, 81.80%. F-measure was as follows: CM1, 0.95; JM1, 0.87; KC1, 0.83; KC2, 0.82; KC3, 0.85; KC4, 0.86; MC1, 0.99; MC2, 0.76; MW1, 0.95; PC1, 0.99; PC2, 0.99; PC3, 0.97; PC4, 0.95; and PC5, 0.80. The accuracy obtained was as follows: CM1, 93.99%; JM1, 84.87%; KC1, 83.25%; KC2, 79.11%; KC3, 78.25%; KC4, 83.18%; MC1, 99.01%; MC2, 70.18%; MW1, 88.90%; PC1, 98.99%; PC2, 99.80%; PC3, 94.11%; PC4, 94.4%; and PC5, 79.02%. The proposed method results were compared with the result obtained from different methods. The proposed model obtained better results than other methods for data set CM1, KC4, MC1, PC1, PC2, PC3, PC4, and PC5.

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RETRACTED ARTICLE: Human character identification using facial features and optimised deep convolutional neural network

Adapa

Enireddy

2022

Multimed Tools Appl

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Optimal feature selection with hybrid classification for automatic face shape classification using fitness sorted Grey wolf update

Cited by 4 publications

References 38 publications

A Review of Methodologies in Machine Learning For Face Shape Analysis

A Review of Methodologies in Machine Learning For Face Shape Analysis

Software Defect Prediction through Neural Network and Feature Selections

RETRACTED ARTICLE: Human character identification using facial features and optimised deep convolutional neural network

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