A convolutional neural network approach for face verification

Khalil-Hani, Mohamed; Sung, Liew Shan

doi:10.1109/hpcsim.2014.6903759

Cited by 31 publications

(10 citation statements)

References 12 publications

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“…The brain is also capable of integrating this multimodal information and generates a unique representation of the visual and auditory stimuli. The simulation of this process in computer systems can be achieved by neural models, particularly ones that are able to create a hierarchy of feature representations such as Convolutional Neural Networks (CNNs), introduced by Lecun, Bottou, Bengio, and Haffner (1998) and used for different visual tasks, as demonstrated by the works of Lawrence, Giles, Tsoi, and Back (1997), Karnowski, Arel, and Rose (2010) and Khalil-Hani and Sung (2014), and auditory tasks in the works of Sainath et al (2015), Li, Chan, and Chun (2010) and Schluter and Bock (2014).…”

Section: Introductionmentioning

confidence: 99%

Developing crossmodal expression recognition based on a deep neural model

Barros

Wermter

2016

Adaptive Behavior

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A robot capable of understanding emotion expressions can increase its own capability of solving problems by using emotion expressions as part of its own decision-making, in a similar way to humans. Evidence shows that the perception of human interaction starts with an innate perception mechanism, where the interaction between different entities is perceived and categorized into two very clear directions: positive or negative. While the person is developing during childhood, the perception evolves and is shaped based on the observation of human interaction, creating the capability to learn different categories of expressions. In the context of human–robot interaction, we propose a model that simulates the innate perception of audio–visual emotion expressions with deep neural networks, that learns new expressions by categorizing them into emotional clusters with a self-organizing layer. The proposed model is evaluated with three different corpora: The Surrey Audio–Visual Expressed Emotion (SAVEE) database, the visual Bi-modal Face and Body benchmark (FABO) database, and the multimodal corpus of the Emotion Recognition in the Wild (EmotiW) challenge. We use these corpora to evaluate the performance of the model to recognize emotional expressions, and compare it to state-of-the-art research.

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Section: Introductionmentioning

confidence: 99%

Developing crossmodal expression recognition based on a deep neural model

Barros

Wermter

2016

Adaptive Behavior

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“…Pérez-Rosas, et al [21] also developed classification models using linguistic features such as lexical, syntactic, and semantic level features and a linear SVM to detect fake and real news. CNN have been utilised in a variety of computer vision in recent years, and they have improved the state-of-theart performance of a variety of visual classification tasks, such as image processing [22], face verification [23], object recognition [24], and natural language processing tasks [25].…”

Section: Related Workmentioning

confidence: 99%

OPCNN-FAKE: Optimized Convolutional Neural Network for Fake News Detection

Saleh¹,

Alharbi

Alsamhi

2021

IEEE Access

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Recently, there is a rapid and wide increase in fake news, defined as provably incorrect information spread with the goal of fraud. The spread of this type of misinformation is a severe danger to social cohesiveness and well-being since it increases political polarisation and people's distrust of their leaders. Thus, fake news is a phenomenon that is having a significant impact on our social lives, particularly in politics. This paper proposes novel approaches based on Machine Learning (ML) and Deep Learning (DL) for the fake news detection system to address this phenomenon. The main aim of this paper is to find the optimal model that obtains high performance. Therefore, we propose an optimized Convolutional Neural Network model to detect fake news (OPCNN-FAKE). We compare the performance of the OPCNN-FAKE with Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and The six regular ML techniques: Decision Tree (DT), logistic Regression (LR), K Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes (NB) using four fake news benchmark datasets. Grid search and hyperopt optimization techniques have been used to optimize the parameters of ML and DL, respectively. In addition, N-gram and Term Frequency-Inverse Document Frequency (TF-IDF) have been used to extract features from the benchmark datasets for regular ML, while Glove word embedding has been used to represent features as a feature matrix for DL models. To evaluate the performance of the OPCNN-FAKE, accuracy, precision, recall, F1-measure were applied to validate the results. The results show that OPCNN-FAKE model has achieved the best performance for each dataset compared with other models. Furthermore, the OPCNN-FAKE has a higher performance of cross-validation results and testing results than the other models, which indicates that the OPCNN-FAKE for fake news detection is significantly better than other models.

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“…[14]. In present days, adequate applications are centered on CNN models, as for example are face recognition [10], [13], semantic parsing, text classification, question answering, information extraction [11], [12], Real-time parking management, traffic control [15], [16], video recognition [17], and Sketch-based image retrieval [18]. Human beings are liable for five senses to understand the actions around us.…”

Section: Cnn Architecturementioning

confidence: 99%

Hand Calculator System using Convolutional Neural Networks

Jhansi*,

Harish

2020

IJRTE

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Computer vision has great attention in recent years as it identifies and similarly processes images that human vision does and they provide suitable output. In computer vision, hand gesture recognition is one of the important and fundamental problems. The hand gesture recognition system has gained significant importance in the recent few years because of its manifoldness applications. This paper aims to give a new approach for vision-based, fast and real time hand gesture recognition, a new light that can be used in many HCI applications. The proposed algorithm first detects and segments the hand region and by using our innovative approach, it finds the fingers and classifies the gesture. The proposed algorithm is invariant to orientation, hand position or distance from the webcam. Based on this proposed algorithm we have progressively developed a gesture-based mathematical tool (calculator) as a practical application.

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A convolutional neural network approach for face verification

Cited by 31 publications

References 12 publications

Developing crossmodal expression recognition based on a deep neural model

Developing crossmodal expression recognition based on a deep neural model

OPCNN-FAKE: Optimized Convolutional Neural Network for Fake News Detection

Hand Calculator System using Convolutional Neural Networks

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