Facial expression recognition using Gabor filter and multi-layer artificial neural network

Verma, Kunika; Khunteta, Ajay

doi:10.1109/icomicon.2017.8279123

Cited by 24 publications

(9 citation statements)

References 2 publications

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“…It extracts both time and frequency domains [102] of the image. Means, it analyzes whether there is any particular frequency content in the image in a particular direction around the point of analysis.…”

Section: ) Gabor Filtermentioning

confidence: 99%

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Facial Sentiment Analysis Using AI Techniques: State-of-the-Art, Taxonomies, and Challenges

et al. 2020

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With the advancements in machine and deep learning algorithms, the envision of various critical real-life applications in computer vision becomes possible. One of the applications is facial sentiment analysis. Deep learning has made facial expression recognition the most trending research fields in computer vision area. Recently, deep learning-based FER models have suffered from various technological issues like under-fitting or over-fitting. It is due to either insufficient training and expression data. Motivated from the above facts, this paper presents a systematic and comprehensive survey on current state-of-art Artificial Intelligence techniques (datasets and algorithms) that provide a solution to the aforementioned issues. It also presents a taxonomy of existing facial sentiment analysis strategies in brief. Then, this paper reviews the existing novel machine and deep learning networks proposed by researchers that are specifically designed for facial expression recognition based on static images and present their merits and demerits and summarized their approach. Finally, this paper also presents the open issues and research challenges for the design of a robust facial expression recognition system. INDEX TERMS Facial sentiment analysis, machine learning, deep learning, convolutional neural network, deep belief network, artificial intelligence.

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Section: ) Gabor Filtermentioning

confidence: 99%

“…The filters having real and imaginary components represents the orthogonal directions. The equations are shown below [102]:…”

Section: ) Gabor Filtermentioning

confidence: 99%

Facial Sentiment Analysis Using AI Techniques: State-of-the-Art, Taxonomies, and Challenges

et al. 2020

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“…Kiran Talele et al, (2016) [5] uses LBP for feature extraction and artificial neural network(ANN) for classification. Kunika and Ajay (2017) [6] Here the R value using FFNN-BR algorithm works good in identifying the emotion with the value nearly equal to 1. Here the Error plot for FFNN-BR shows the almost horizontal line which indicates the betterment in network performance when compared to FFNN-BP algorithm…”

Section: Introductionmentioning

confidence: 93%

Human Emotion Identification Using F eed Forward Neural Network with Backpropagation and Bayesian Regularized Backpropagation Algorithm

Sofia*,

Sivakumar

2019

IJEAT

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the facial features will be extracted and classified into different expressions. Here two types of algorithm viz. used in Feed Forward neural network(FFNN), ie., Backpropagation(BP) Algorithm and Bayesian Regularization Algorithm. After evaluating Bayesian regularized Backpropagation Algorithm (BR) is found to be better suited for automatic facial expression recognition than Backpropagation algorithm(BP), and the performance is evaluate using various metrics.

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“…In their work, LBP was used to extract the features of several important parts of the face, and then these features were connected to form feature vectors for input to neural networks. Verma and Khunteta et al [22] proposed a method that used Gabor filter to extract facial expression fields in space and then input them into an artificial neural network (ANN) for recognition. Uddin et al [23] proposed a new feature extraction method called local directional position pattern (LDPP) that can simultaneously maintain the characteristics of the bright and dark areas of an image.…”

Section: A Traditional Manual Featuresmentioning

confidence: 99%

ROI-Attention Vectorized CNN Model for Static Facial Expression Recognition

Sun

Zheng

2020

IEEE Access

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When using neural networks to recognize facial expressions, determining which features help identify different expressions is essential, and there is a massive information transmission loss between layers of network with multiple layers. This paper proposes a robust vectorized convolutional neural network (CNN) model that introduces an attention mechanism for extracting features in the region of interests(ROIs) of the face. The ROIs in the facial image are marked before the image is input into the neural network. In particular, the attention concept is adopted in the first layer of the proposed neural network to perform ROIs-related convolution calculation, and ROIs-related convolution calculation results of the specific fields in the ROIs are increased by extracting more robust features. Next, the idea of features' vectors inspired by CapsNet is used in the following layer of the proposed neural network. Multi-level convolutions are used to extract feature vectors of different ROIs for facial expression, and then the feature vectors are reconstructed by a decoder to reconstruct the image. Comprehensive comparative experiments and cross-database experiments are conducted to verify the validity and robustness of our proposed model. The experimental results also demonstrate that our method is very effective in improving the performance of facial expressions recognition. INDEX TERMS Regions of interests, attention mechanism, facial expression recognition, vectored features, CapsNet.

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Facial expression recognition using Gabor filter and multi-layer artificial neural network

Cited by 24 publications

References 2 publications

Facial Sentiment Analysis Using AI Techniques: State-of-the-Art, Taxonomies, and Challenges

Facial Sentiment Analysis Using AI Techniques: State-of-the-Art, Taxonomies, and Challenges

Human Emotion Identification Using F eed Forward Neural Network with Backpropagation and Bayesian Regularized Backpropagation Algorithm

ROI-Attention Vectorized CNN Model for Static Facial Expression Recognition

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