Gabor-oriented local order feature-based deep learning for face annotation

Kasthuri, A.; Suruliandi, A.; Raja, S. P.

doi:10.1142/s0219691319500322

Cited by 10 publications

(5 citation statements)

References 34 publications

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“…Jia et al [18] used a two-stage recurrent neural network to extract shape and texture features, while Kasthuri et al [19] combined deep learning with Gabor filters for face recognition. Simon et al [20] combined deep architecture features with luminance information.…”

Section: Methodsmentioning

confidence: 99%

Attention-Guided Deep Learning Texture Feature for Object Recognition Applications

Veerashetty

2023

RAiSE-2023

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Section: Methodsmentioning

confidence: 99%

Attention-Guided Deep Learning Texture Feature for Object Recognition Applications

Veerashetty

2023

RAiSE-2023

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“…The basic principle of gray-level co-occurrence matrix is the elements in the GLCM, which represents the joint distribution of the gray levels of two pixels with a particular spatial position relationship. Academic interpretation refers to the process of extracting texture feature parameters by specific image processing technology to obtain the quantitative or qualitative description of the texture [ 8 ].…”

Section: Methodsmentioning

confidence: 99%

Age prediction based on a small number of facial landmarks and texture features

Wang¹,

Chen²

2021

THC

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BACKGROUND: Age is an essential feature of people, so the study of facial aging should have particular significance. OBJECTIVE: The purpose of this study is to improve the performance of age prediction by combining facial landmarks and texture features. METHODS: We first measure the distribution of each texture feature. From a geometric point of view, facial feature points will change with age, so it is essential to study facial feature points. We annotate the facial feature points, label the corresponding feature point coordinates, and then use the coordinates of feature points and texture features to predict the age. RESULTS: We use the Support Vector Machine regression prediction method to predict the age based on the extracted texture features and landmarks. Compared with facial texture features, the prediction results based on facial landmarks are better. This suggests that the facial morphological features contained in facial landmarks can reflect facial age better than facial texture features. Combined with facial landmarks and texture features, the performance of age prediction can be improved. CONCLUSIONS: According to the experimental results, we can conclude that texture features combined with facial landmarks are useful for age prediction.

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“…Few studies in the state-of-the-art have employed feature-based techniques for annotation in interdisciplinary domains [6,30]. However, the majority of studies for this domain (i.e., fake news) have done manual data annotation through human annotators with domain expertise.…”

Section: Popularitymentioning

confidence: 99%

Disinformation detection on social media: An integrated approach

Rastogi

Bansal

2022

Multimed Tools Appl

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The emergence of social media platforms has amplified the dissemination of false information in various forms. Social media gives rise to virtual societies by providing freedom of expression to users in a democracy. Due to the presence of echo chambers on social media, social science studies play a vital role in the spread of false news. To this aim, we provide a comprehensive framework that is adapted from several scholarly studies. The framework is capable of detecting information into various types, namely real, disinformation and satire based on authenticity as well as intention. The process highlights the use of interdisciplinary approaches derived from fundamental theories of social science and integrating them with modern computational tools and techniques. Few of these theories claim that malicious users suggest writing fabricated content in a different style to attract the audience. Stylebased methods evaluate the intention i.e., the content is written with an intent to mislead the audience or not. However, the writing style can be deceptive. Thus, it is important to involve user-oriented social information to improve model strength. Therefore, the paper used an integrated approach by combining style based and propagation-based features with a total of thirty-one features. The extracted features are divided into ten categories: relative frequency, quantity, complexity, uncertainty, sentiment, subjectivity, diversity, informality, additional, and popularity. The features have been iteratively utilized by supervised classifiers and then selected the best-correlated ones using the ANOVA test. Our experimental results have shown that the selected features are able to distinguish real from disinformation and satirical news. It has been observed that the Ensemble machine learning model outperformed other models over the developed multi-labelled corpus.

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Gabor-oriented local order feature-based deep learning for face annotation

Cited by 10 publications

References 34 publications

Attention-Guided Deep Learning Texture Feature for Object Recognition Applications

Attention-Guided Deep Learning Texture Feature for Object Recognition Applications

Age prediction based on a small number of facial landmarks and texture features

Disinformation detection on social media: An integrated approach

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