Facial Emotion Recognition Using NLPCA and SVM

Reddy, Chirra Venkata Rami; Reddy, U. Srinivasulu; Kolli, Venkata Krishna Kishore

doi:10.18280/ts.360102

Cited by 29 publications

(17 citation statements)

References 13 publications

(13 reference statements)

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“…The recent approaches in computer vision, especially in the fields of machine learning and deep learning have improved the efficiency of image classification tasks [1][2][3][4][5][6]. Detection of defected fruits and the classification of fresh and rotten fruits represent one of the major challenges in the agricultural fields.…”

Section: Introductionmentioning

confidence: 99%

Fresh and Rotten Fruits Classification Using CNN and Transfer Learning

Palakodati¹,

Chirra²,

Dasari³

et al. 2020

RIA

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Detecting the rotten fruits become significant in the agricultural industry. Usually, the classification of fresh and rotten fruits is carried by humans is not effectual for the fruit farmers. Human beings will become tired after doing the same task multiple times, but machines do not. Thus, the project proposes an approach to reduce human efforts, reduce the cost and time for production by identifying the defects in the fruits in the agricultural industry. If we do not detect those defects, those defected fruits may contaminate good fruits. Hence, we proposed a model to avoid the spread of rottenness. The proposed model classifies the fresh fruits and rotten fruits from the input fruit images. In this work, we have used three types of fruits, such as apple, banana, and oranges. A Convolutional Neural Network (CNN) is used for extracting the features from input fruit images, and Softmax is used to classify the images into fresh and rotten fruits. The performance of the proposed model is evaluated on a dataset that is downloaded from Kaggle and produces an accuracy of 97.82%. The results showed that the proposed CNN model can effectively classify the fresh fruits and rotten fruits. In the proposed work, we inspected the transfer learning methods in the classification of fresh and rotten fruits. The performance of the proposed CNN model outperforms the transfer learning models and the state of art methods.

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Section: Introductionmentioning

confidence: 99%

Fresh and Rotten Fruits Classification Using CNN and Transfer Learning

Palakodati¹,

Chirra²,

Dasari³

et al. 2020

RIA

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show abstract

“…For instance, Benkaddour and Bounoua [17] conducted feature extracted with deep convolutional neural network (DCNN) and completed face recognition by the PCA and support vector classifier (SVC). Reddy et al [18] suggested recognizing facial emotions with nonlinear principal component analysis (NLPCA) and support vector machine (SVM). All these methods shed new lights on the identification of small samples, and will be referred to in our future research.…”

Section: Discussionmentioning

confidence: 99%

A Neighborhood Structure-Preserving Bi-objective Optimization Method Based on Class Center and Discriminant Analysis and Its Application in Facial Recognition

Ma¹

2019

RIA

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Based on class center and discriminant analysis, this paper puts forward a novel bi-objective optimization method that preserves the neighborhood structure, and applies it to facial recognition. Firstly, the locally preserving projection (LPP) was improved into the class-center locally preserving projection (CLPP) by replacing the sample-based neighborhood structure with the class center-based neighborhood structure. Next, a bi-objective optimization model was developed based on the CLPP and linear discriminant analysis (LDA), and solved by the multi-objective optimization theory. The bi-objective optimization problem combines the merits of single-target CLPP and single-target LDA: the class center-based neighborhood structure is preserved, and the class information is introduced naturally, making up for the defect of the LDA due to the manual changes of adjacency coefficient and highlighting the physical meaning. Finally, several experiments were conducted on AR, CAS-60 and FERET face databases. The experimental results prove that our methods are correct and effective, and the bi-objective optimization method based on CLPP and LDA (CLPP+LDA) achieved the best recognition effect.

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“…For our experiments, the word vector dimension was set as 128, the size of the hidden LSTM layer as 256, the activation function as ReLU, and the minimum batch size of training as 16. A total of 100 kernels were selected from the value range of { (2,3,4), (3,4,5), (4,5,6)}. Adam was chosen as the optimization function, the learning rate was initialized as 0.001, and the cross entropy was defined as the loss function.…”

Section: Hyperparameter Settingsmentioning

confidence: 99%

“…The popular traditional ML methods include naive Bayes [3], decision tree (DT) [4], k-nearest neighbors (k-NN) [5], support vector machine (SVM) [6], etc. These methods usually represent the text as high-dimensional sparse vectors, which requires manual annotation to construct features [7].…”

Section: Introductionmentioning

confidence: 99%

Text Sentiment Classification Based on Feature Fusion

Zhang¹,

Li²,

Cheng³

2020

RIA

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The convolutional neural network (CNN) and long short-term memory (LSTM) network are adept at extracting local and global features, respectively. Both can achieve excellent classification effects. However, the CNN performs poorly in extracting the global contextual information of the text, while LSTM often overlooks the features hidden between words. For text sentiment classification, this paper combines the CNN with bidirectional LSTM (BiLSTM) into a parallel hybrid model called CNN_BiLSTM. Firstly, the CNN was adopted to extract the local features of the text quickly. Next, the BiLSTM was employed to obtain the global text features containing contextual semantics. After that, the features extracted by the two neural networks (NNs) were fused, and processed by Softmax classifier for text sentiment classification. To verify its performance, the CNN_BiLSTM was compared with single NNs like CNN and LSTM, as well as other deep learning (DL) NNs through experiments. The experimental results show that the proposed parallel hybrid model outperformed the contrastive methods in F1-score and accuracy. Therefore, our model can solve text sentiment classification tasks effectively, and boast better practical value than other NNs.

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Facial Emotion Recognition Using NLPCA and SVM

Cited by 29 publications

References 13 publications

Fresh and Rotten Fruits Classification Using CNN and Transfer Learning

Fresh and Rotten Fruits Classification Using CNN and Transfer Learning

A Neighborhood Structure-Preserving Bi-objective Optimization Method Based on Class Center and Discriminant Analysis and Its Application in Facial Recognition

Text Sentiment Classification Based on Feature Fusion

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