scholarly journals Siamese Architecture-Based 3D DenseNet with Person-Specific Normalization Using Neutral Expression for Spontaneous and Posed Smile Classification

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7184
Author(s):  
Kunyoung Lee ◽  
Eui Chul Lee
Keyword(s):  
Principal Component ◽  
Facial Muscle ◽  
Automated Classification ◽  
Video Classification ◽  
Model Accuracy ◽  
Proposed Model ◽  
3D Cnn ◽  
Reference Input ◽  
Neutral Expression

Clinical studies have demonstrated that spontaneous and posed smiles have spatiotemporal differences in facial muscle movements, such as laterally asymmetric movements, which use different facial muscles. In this study, a model was developed in which video classification of the two types of smile was performed using a 3D convolutional neural network (CNN) applying a Siamese network, and using a neutral expression as reference input. The proposed model makes the following contributions. First, the developed model solves the problem caused by the differences in appearance between individuals, because it learns the spatiotemporal differences between the neutral expression of an individual and spontaneous and posed smiles. Second, using a neutral expression as an anchor improves the model accuracy, when compared to that of the conventional method using genuine and imposter pairs. Third, by using a neutral expression as an anchor image, it is possible to develop a fully automated classification system for spontaneous and posed smiles. In addition, visualizations were designed for the Siamese architecture-based 3D CNN to analyze the accuracy improvement, and to compare the proposed and conventional methods through feature analysis, using principal component analysis (PCA).

scholarly journals Automated Classification of the Tympanic Membrane Using a Convolutional Neural Network

2019 ◽  
Vol 9 (9) ◽  
pp. 1827 ◽  
Author(s):  
Je Yeon Lee ◽  
Seung-Ho Choi ◽  
Jong Woo Chung
Keyword(s):  
Tympanic Membrane ◽  
Automated Classification ◽  
Model Accuracy ◽  
Test Dataset ◽  
Real Time Analysis ◽  
Medical Image Recognition ◽  
Test Sets ◽  
High Level ◽  
Level Analysis

Precise evaluation of the tympanic membrane (TM) is required for accurate diagnosis of middle ear diseases. However, making an accurate assessment is sometimes difficult. Artificial intelligence is often employed for image processing, especially for performing high level analysis such as image classification, segmentation and matching. In particular, convolutional neural networks (CNNs) are increasingly used in medical image recognition. This study demonstrates the usefulness and reliability of CNNs in recognizing the side and perforation of TMs in medical images. CNN was constructed with typically six layers. After random assignment of the available images to the training, validation and test sets, training was performed. The accuracy of the CNN model was consequently evaluated using a new dataset. A class activation map (CAM) was used to evaluate feature extraction. The CNN model accuracy of detecting the TM side in the test dataset was 97.9%, whereas that of detecting the presence of perforation was 91.0%. The side of the TM and the presence of a perforation affect the activation sites. The results show that CNNs can be a useful tool for classifying TM lesions and identifying TM sides. Further research is required to consider real-time analysis and to improve classification accuracy.

scholarly journals An Efficient Approach for Segmentation and Classification of Acute Lymphoblastic Leukemia via Optimized Spike-based Network

2021 ◽  
Author(s):  
Ali Noshad ◽  
saeed fallahi
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Blood Cells ◽  
Lymphoblastic Leukemia ◽  
Gaussian Function ◽  
Principal Component ◽  
Complex Issue ◽  
Morphological Variations ◽  
Learning Techniques ◽  
Proposed Model

Abstract Identification of uncontrolled accumulation of abnormal blood cells ( lymphoblasts ) considered to be a challenging task. Despite a wide variety of image processing and deep learning techniques, the task of extracting the features from Acute Lymphoblastic Leukemia (ALL) images and detection of ALL cells is still challenging and complex issue due to morphological variations in cells. In order to overcome these drawbacks, in this study, we proposed a new framework with a combination of spiking and residual network for the detection and classification of lymphoblasts cells from healthy ones in blood sample images. According to this, features are extracted using a novel First-Spike-based approach, and then the Gaussian function is applied to remove the low-intensity edges. To reduce dimensionality, Principal Component Analysis ( PCA ) is used and finally, a developed deep residual architecture is employed to diagnose the ALL blood cells from the reconstructed images. To show the effectiveness of the proposed model, it is evaluated on microscopic images of blood samples from ALL Images (ALL- IDB ) and ISBI -2019 C- NMC dataset. The results show the superiority of the model to be an appropriate choice for future biomedical imaging tasks.

Recognition of Facial Expression via Kernel PCA Network

2014 ◽  
Vol 631-632 ◽  
pp. 498-501 ◽  
Author(s):  
De Kun Hu ◽  
An Sheng Ye ◽  
Li Li ◽  
Li Zhang
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Expression Recognition ◽  
Kernel Pca ◽  
Face Expression ◽  
Proposed Model

In this work, a kernel principle component analysis network (KPCANet) is proposed for classification of the facial expression in unconstrained images, which comprises only the very basic data processing components: cascaded kernel principal component analysis (KPCA), binary hashing, and block-wise histograms. In the proposed model, KPCA is employed to learn multistage filter banks. It is followed by simple binary hashing and block histograms for indexing and pooling. For comparison and better understanding, We have tested these basic networks extensively on many benchmark visual datasets ( such as the JAFFE [13] database, the CMU AMP face expression database, a part of the Extended Cohn-Kanade (CK+) database), The results demonstrate the potential of the KPCANet serving as a simple but highly competitive baseline for facial expression recognition.

scholarly journals An ANN Approach to Classification of Galaxy Spectra for the 2DF Galaxy Redshift Survey

1999 ◽  
Vol 183 ◽  
pp. 154-154
Author(s):  
S.R. Folkes ◽  
O. Lahav ◽  
S.J. Maddox
Keyword(s):  
Principal Component ◽  
Automated Classification ◽  
Signal To Noise ◽  
Normal Galaxies ◽  
Redshift Surveys ◽  
Redshift Survey ◽  
Galaxy Redshift ◽  
Spectral Simulations ◽  
Artificial Neural Network Ann

We present a method for automated classification of galaxies with low signal-to-noise (S/N) spectra typical of redshift surveys. We develop spectral simulations based on the parameters for the 2dF Galaxy Redshift Survey and investigate the technique of Principal Component Analysis when applied to spectra of low S/N. It is found that the projection onto the first 8 Principal Components hold most of the real spectral information, with later projections only adding noise. Using these components as input, we train an Artificial Neural Network (ANN) to classify the noisy simulated spectra into morphological classes. We find that more than 90% of our sample of normal galaxies are correctly classified into one of five morphological classes for simulations at bJ=19.7.

DETECTION DESTRUCTIVE CONTENT IN SOCIAL MEDIA BASED ON MACHINE LEARNING MODELS

2021 ◽  
pp. 7-20
Author(s):  
Владимир Александрович Минаев ◽  
Алена Дмитриевна Реброва ◽  
Александр Викторович Симонов
Keyword(s):  
Social Media ◽  
System Integration ◽  
Personal Data ◽  
Principal Component ◽  
Feature Space ◽  
User Profile ◽  
Bag Of Words ◽  
Automated Classification ◽  
Learning Models

В статье обсуждаются модели классификации текстового контента и методы его предварительной обработки с целью выявления деструктивных воздействий в социальных медиа. Показано, что основным источником деструктивного контента выступает профиль пользователей, характеризующийся набором личным данных, содержанием публикаций, параметрами сообщества, аккаунтов сети, сообщений и чатов. Говорится об актуальности автоматизированного сбора и анализа данных с помощью моделей прецедентного и дедуктивного обучения. Рассматриваются их основные разновидности и задачи, решаемые на их основе, включающие прогнозирование и типологизацию в аспекте деструктивного содержания текстов, снижение размерности признаков их описания. Исследованы и применены основные методы векторизации текстов: Bag of Words, TF_IDF, Word2vec. На практических корпусах текстов из социальной сети ВКонтакте решены задачи выявления деструктивного контента, связанного с радикальным исламом. Показано, что с помощью примененных моделей и методов все тексты, включающие деструктивный контент, классифицированы верно. Наиболее высокую точность (0,97) при решении задачи распознавания деструктивного контента дает системная интеграция алгоритма векторизации Bag of Words, метода главных компонент для снижения пространства признаков описания текстов и логистической регрессии или случайного леса как моделей обучения. Сделан вывод, что наборы данных, имеющие связь с исламским радикализмом, характеризуются достаточно четкими признаками, которые хорошо вычисляемы с помощью современных моделей, методов и алгоритмов, и могут эффективно применяться для автоматизированной классификации текстовых массивов с целью выявления их деструктивной направленности. Развитие направления, представленного в статье, связано с увеличением исследуемых корпусов документов, более детальным анализом текстов на основе сложных моделей распознавания латентной экстремистской пропаганды, в том числе - представленной в фото, аудио- и видеоформатах. The article discusses models of classification of text content and methods of its pre-processing in order to identify destructive influences in social media. It is shown that the main source of destructive content is the user profile, which is characterized by a set of personal data, the content of publications, community parameters, network accounts, messages and chats. Automated data collection and analysis using case-based and deductive learning models is discussed. We consider their main varieties and the tasks solved on their basis, including forecasting and typology in the aspect of the destructive content of texts, reducing the dimension of the features of their description. The main methods of text vectorization are investigated and applied: Bag of Words, TF_IDF, Word2vec. The tasks of identifying destructive content related to Islamic radicalism are solved on the practical corpus of texts from the social network VKontakte. It is shown that using the applied models and methods, all texts that include destructive content are classified correctly. The highest accuracy (0.97) in solving the problem of recognizing destructive content is provided by the system integration of the Bag of Words vectorization algorithm, the principal component method for reducing the feature space of text descriptions, and logistic regression or random forest as learning models. It is concluded that the data sets associated with Islamic radicalism are characterized by sufficiently clear features that are well calculated using modern models, methods and algorithms, and can be effectively used for automated classification of text arrays in order to identify their destructive orientation. The development of the direction presented in the article is associated with an increase in the studied corpus of documents, a more detailed analysis of texts based on complex models for recognizing latent extremist propaganda, including those presented in photo, audio and video formats.

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