A robust feature extraction with optimized DBN-SMO for facial expression recognition

Ramachandran, Vedantham; Reddy, E. Sreenivasa

doi:10.1007/s11042-020-08901-x

Cited by 15 publications

(8 citation statements)

References 46 publications

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“…In the last few years, lots of researchers have applied the DBN in many application fields such as image recognition, speech recognition, face recognition, and plant leaf classification ( Cheng et al, 2018 ; Goudarzi et al, 2018 ; Liu et al, 2018 ; Cristin et al, 2020 ; Vedantham and Reddy, 2020 ; Veisi and Mani, 2020 ; Chen and Pan, 2021 ). It is a multi-layer network structure, which provides a basic model for feature extraction and subsequent image recognition of original images.…”

Section: Related Workmentioning

confidence: 99%

Image Classification and Recognition of Rice Diseases: A Hybrid DBN and Particle Swarm Optimization Algorithm

Lü

Liu

et al. 2022

Front. Bioeng. Biotechnol.

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Rice blast, rice sheath blight, and rice brown spot have become the most popular diseases in the cold areas of northern China. In order to further improve the accuracy and efficiency of rice disease diagnosis, a framework for automatic classification and recognition of rice diseases is proposed in this study. First, we constructed a training and testing data set including 1,500 images of rice blast, 1,500 images of rice sheath blight, and 1,500 images of rice brown spot, and 1,100 healthy images were collected from the rice experimental field. Second, the deep belief network (DBN) model is designed to include 15 hidden restricted Boltzmann machine layers and a support vector machine (SVM) optimized with switching particle swarm (SPSO). It is noted that the developed DBN and SPSO-SVM can simultaneously learn three proposed features including color, texture, and shape to recognize the disease type from the region of interest obtained by preprocessing the disease images. The proposed model leads to a hit rate of 91.37%, accuracy of 94.03%, and a false measurement rate of 8.63%, with the 10-fold cross-validation strategy. The value of the area under the receiver operating characteristic curve (AUC) is 0.97, whose accuracy is much higher than that of the conventional machine learning model. The simulation results show that the DBN and SPSO-SVM models can effectively extract the image features of rice diseases during recognition, and have good anti-interference and robustness.

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Section: Related Workmentioning

confidence: 99%

Image Classification and Recognition of Rice Diseases: A Hybrid DBN and Particle Swarm Optimization Algorithm

Lü

Liu

et al. 2022

Front. Bioeng. Biotechnol.

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“…However, the LBP method has an open challenge: it cannot capture a bigger scale structure and might be the dominant facial feature, it is also unable to handle variations in appearance that are generally happen because of a pose, partial occlusion, and expression [19], LBP is also sensitive towards random noise at uniform areas such as cheek and forehead [7,8,15].…”

Section: Related Workmentioning

confidence: 99%

“…The handcrafted descriptor method such as Local Binary Pattern (LBP) is known to be more effective to retain statistical characteristics [14]. The problem of LBP is that it can't capture global features at uniform areas such as cheek and forehead [7,8], [15] that may become a dominant representation. In addition, it is more sensitive to noise compared with other LBP variations such as Local Ternary Pattern (LTP), which was introduced by Tan and Triggs [8] for face recognition.…”

Section: Introductionmentioning

confidence: 99%

Handwritten Character Recognition Using Unsupervised Feature Selection and Multi Support Vector Machine Classifier

2021

IJIES

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“…The Most common existing appearance based feature extraction techniques used for emotion recognition are gabor filter (GF) (Revina and Emmanuel, 2018), principal component analysis (PCA) (Franco and Treves, 2001), independent component analysis (ICA) (Uddin et al, 2009), local binary pattern (LBP) (Shan et al, 2009), local directional ternary pattern (LDTP) (Ramírez Rivera et al, 2015), gabor wavelets (Zhang and Ma, 2007), histogram of oriented gradients (HOG) (Mlakar and Potocnik, 2015), discrete wavelet transform (DWT) (Nayak et al, 2016), discrete cosine transform (DCT) (Vedantham and Reddy, 2020) and stationary wavelet transform (SWT) (Nayak et al, 2017). The Comparative review of these appearance feature extraction techniques used for feature extraction in FERS is reviewed in Table 4.…”

Section: Appearance Based Feature Extraction Techniquesmentioning

confidence: 99%

Systematic review of various feature extraction techniques for facial emotion recognition system

Kumari¹,

Bhatia²

2021

IJIEI

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Facial emotion recognition (FER) is the task of recognising human emotions from images and videos. Communicating through facial emotions is a kind of non-verbal communication and it reflects a person's inner thoughts and mental states. In the present study, various existing geometric and appearance based feature extraction techniques used in FER are reviewed in tabular form. The main motive of this paper is to analyse the performance of these techniques on the bases of accuracy on different datasets like JAFFE, CK+, CK and MMI. After extensive research on feature extraction techniques for FERS, it is found that the appearance feature-based techniques achieved maximum accuracy and more favourable as compared to geometric feature-based techniques. Finally, the paper concludes with the various challenges encountered for feature extraction in the field of FERS which need to be addressed in the future.

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A robust feature extraction with optimized DBN-SMO for facial expression recognition

Cited by 15 publications

References 46 publications

Image Classification and Recognition of Rice Diseases: A Hybrid DBN and Particle Swarm Optimization Algorithm

Image Classification and Recognition of Rice Diseases: A Hybrid DBN and Particle Swarm Optimization Algorithm

Handwritten Character Recognition Using Unsupervised Feature Selection and Multi Support Vector Machine Classifier

Systematic review of various feature extraction techniques for facial emotion recognition system

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