Facial Expression Recognition Using Weighted Mixture Deep Neural Network Based on Double-Channel Facial Images

Yang, Biao; Cao, Jinmeng; Ni, Rongrong; Zhang, Yuyu

doi:10.1109/access.2017.2784096

Cited by 176 publications

(63 citation statements)

References 31 publications

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“…[ 28 ], the authors present an observing framework using some features, such as LBP/LTP/red blood cell (RBC) for children, which utilizes an automatic pain detection system, and it could be accessed through wearable or mobile devices. A weighted fusion strategy [ 5 ] is proposed to completely utilize the features that were separated from various image channels with a partial Visual Geometry Group called the VGG16 network. Moreover, the method can develop consequently for extracting features of images on account of an absence of successful pre-prepared models dependent on LBP.…”

Section: Resultsmentioning

confidence: 99%

“…Researchers have worked with many neural networking concepts like Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and machine learning classifiers like Support Vector Machine (SVM), K Nearest Neighbour (KNN) to find, relatively, the most accurate FER technique. In connection to this, several researchers utilized the Neural Network based on different kinds of popular methods like CNN [ 1 ], CNN-RNN [ 2 ], 3DCNN-DAP [ 3 , 4 ], Weighted Mixture Deep Neural Network [ 5 ], CNN with attention mechanism (ACNN) where it empowers the model to move consideration from the impeded patches to other unhampered ones, just as distinct facial regions are dependent on patch-based ACNN (pACNN) and global-local based ACNN (gACNN) [ 6 ]. Although neural networks are easy to build with the latest programming languages like Python, R, and tools like Matlab and Weka, nevertheless, when it comes to the computational power, especially in facial image processing with many classes, it requires very high processing power with a high amount of random access memory (RAM) and a graphics processing unit (GPU).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Development of a Robust Multi-Scale Featured Local Binary Pattern for Improved Facial Expression Recognition

Yasmin

Pathan

Biswas

et al. 2020

Sensors

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Compelling facial expression recognition (FER) processes have been utilized in very successful fields like computer vision, robotics, artificial intelligence, and dynamic texture recognition. However, the FER’s critical problem with traditional local binary pattern (LBP) is the loss of neighboring pixels related to different scales that can affect the texture of facial images. To overcome such limitations, this study describes a new extended LBP method to extract feature vectors from images, detecting each image from facial expressions. The proposed method is based on the bitwise AND operation of two rotational kernels applied on LBP(8,1) and LBP(8,2) and utilizes two accessible datasets. Firstly, the facial parts are detected and the essential components of a face are observed, such as eyes, nose, and lips. The portion of the face is then cropped to reduce the dimensions and an unsharp masking kernel is applied to sharpen the image. The filtered images then go through the feature extraction method and wait for the classification process. Four machine learning classifiers were used to verify the proposed method. This study shows that the proposed multi-scale featured local binary pattern (MSFLBP), together with Support Vector Machine (SVM), outperformed the recent LBP-based state-of-the-art approaches resulting in an accuracy of 99.12% for the Extended Cohn–Kanade (CK+) dataset and 89.08% for the Karolinska Directed Emotional Faces (KDEF) dataset.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of a Robust Multi-Scale Featured Local Binary Pattern for Improved Facial Expression Recognition

Yasmin

Pathan

Biswas

et al. 2020

Sensors

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show abstract

“…In comparison to the existing methodologies, this framework was 15 times faster than the other recognition frameworks [31]. As per the face location framework portrayed by Rowley et al, a Multiclassifier-based Near-Real-Time Face Detection System is the fastest algorithm in detecting human faces as compared to existing methodologies.…”

Section: Primary Phasementioning

confidence: 92%

“…The eye and face detection module was programmed with the modified feature-based Haar cascade face detection classifier algorithm [13,22,26,28,31,32]. The modified Haar cascade face detection classifier algorithm detects an edge, line, and center-surround features of an intruder object.…”

Section: Primary Phasementioning

confidence: 99%

Smart Home Anti-Theft System: A Novel Approach for Near Real-Time Monitoring and Smart Home Security for Wellness Protocol

Pandya¹,

Ghayvat

Kotecha

et al. 2018

ASI

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The proposed research methodology aims to design a generally implementable framework for providing a house owner/member with the immediate notification of an ongoing theft (unauthorized access to their premises). For this purpose, a rigorous analysis of existing systems was undertaken to identify research gaps. The problems found with existing systems were that they can only identify the intruder after the theft, or cannot distinguish between human and non-human objects. Wireless Sensors Networks (WSNs) combined with the use of Internet of Things (IoT) and Cognitive Internet of Things are expanding smart home concepts and solutions, and their applications. The present research proposes a novel smart home anti-theft system that can detect an intruder, even if they have partially/fully hidden their face using clothing, leather, fiber, or plastic materials. The proposed system can also detect an intruder in the dark using a CCTV camera without night vision capability. The fundamental idea was to design a cost-effective and efficient system for an individual to be able to detect any kind of theft in real-time and provide instant notification of the theft to the house owner. The system also promises to implement home security with large video data handling in real-time. The investigation results validate the success of the proposed system. The system accuracy has been enhanced to 97.01%, 84.13, 78.19%, and 66.5%, in scenarios where a detected intruder had not hidden his/her face, hidden his/her face partially, fully, and was detected in the dark from 85%, 64.13%, 56.70%, and 44.01%.

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“…Following this process, the eye and face detection module sends the captured images to the pixel processing module for further processing. b. Pixel Processing Module: First, if the intruder has partially covered their face, this module detects the brightest region of the face such as the eyes, cheeks, or upper part of the head [19][20]. Next, it detects the motion of the captured intruder using the motion detection module (Mm).…”

Section: IImentioning

confidence: 99%

Smart Home Anti-Theft System: A Novel Approach for Near Real-Time Monitoring, Smart Home Security and Large Video Data Handling for Wellness Protocol

Pandya¹,

Ghayvat²,

Kotecha³

et al. 2018

Preprint

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Abstract:The proposed research methodology aims to design a generally implementable framework for providing a house owner/member with the immediate notification of an on-going theft (unauthorized access to their premises). For this purpose, a rigorous analysis of existing systems was undertaken to identify research gaps. The problems found with existing systems were that they can only identify the intruder after the theft, or cannot distinguish between human and non-human objects. Wireless Sensors Networks (WSNs) combined with the use of Internet of Things (IoT), Cognitive Internet of Things, Internet of Medical Things, and Cloud Computing are expanding smart home concepts and solutions, and their applications. The primary objective of the present research work was to design and develop IoT and cloud computing based smart home solutions. In addition, we also propose a novel smart home anti-theft system that can detect an intruder, even if they have partially/fully hidden their face using clothing, leather, fiber, or plastic materials. The proposed system can also detect an intruder in the dark using a CCTV camera without night vision facility. The fundamental idea was to design a cost-effective and efficient system for an individual to be able to detect any kind of theft in real-time and provide instant notification of the theft to the house owner. The system also promises to implement home security with large video data handling in real-time.

show abstract

Facial Expression Recognition Using Weighted Mixture Deep Neural Network Based on Double-Channel Facial Images

Cited by 176 publications

References 31 publications

Development of a Robust Multi-Scale Featured Local Binary Pattern for Improved Facial Expression Recognition

Development of a Robust Multi-Scale Featured Local Binary Pattern for Improved Facial Expression Recognition

Smart Home Anti-Theft System: A Novel Approach for Near Real-Time Monitoring and Smart Home Security for Wellness Protocol

Smart Home Anti-Theft System: A Novel Approach for Near Real-Time Monitoring, Smart Home Security and Large Video Data Handling for Wellness Protocol

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