Emotion Recognition from Facial Expressions Using Type‐2 Fuzzy Sets

Halder, Arijit; Konar, Amit; Chakraborty, Aruna; Nagar, Atulya K.

doi:10.1002/9781118910566.ch7

Cited by 2 publications

(2 citation statements)

References 54 publications

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“…Most of the BCI systems require huge and costly EEG machines and commercial software's which limit the feasibility of the system for daily applications. [5] A MIDlet program that contains a cognitive detection algorithm is built in the mobiles that continuously monitor the EEG signals acquired from EEG machines, and then recognize the user's state of mind.…”

Section: Literature Surveymentioning

confidence: 99%

EmoTunes: Your Emotional Soundtrack

Tamkhane ,

2024

IJSREM

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The human face is an important organ of an individual’s body and it especially plays an important role in extraction of an individual‘s behavior and emotional state. Manually segregating the list of songs and generating an appropriate playlist based on an individual’s emotional features is a very tedious, time consuming, labor intensive and upheld task. Various algorithms have been proposed and developed for automating the playlist generation process.However the proposed existing algorithms in use are computationally slow, less accurate and sometimes even require use of additional hardware like EEG or sensors. This proposed system based on facial expression extracted will generate a playlist automatically thereby reducing the effort and time involved in rendering the process manually. Thus the proposed system tends to reduce the computational time involved in obtaining the results and the overall cost of the designed system, thereby increasing the overall accuracy of the system. Testing of the system is done on both user dependent (dynamic) and user independent (static) dataset. Facial expressions are captured using an inbuilt camera. The accuracy of the emotion detection algorithm used in the system for real time images is around 85-90%, while for static images it is around 98- 100%.The proposed algorithm on an average calculated estimation takes around 0.95-1.05 sec to generate an emotion based music playlist. Thus, it yields better accuracy in terms of performance and computational time and reduces the designing cost, compared to the algorithms used in the literature survey. Keywords—Audio Emotion Recognition, Music Information Retrieval, Emotion Extraction Module, Audio Feature Extraction Module, Artificial Neural Networks, Confusion Matrix, Viola and Jones Face Detection

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Section: Literature Surveymentioning

confidence: 99%

EmoTunes: Your Emotional Soundtrack

Tamkhane ,

2024

IJSREM

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“…The semantic segmentation of face parts is widely used for personal identification [1,2], the recognition of emotion and facial expression [3,4], and the super-resolution of face images [5]. Recently, some investigators have reported that the convolutional neural network (CNN), which is a deep learning technique, achieves high performance when applied to face part semantic segmentation.…”

Section: Introductionmentioning

confidence: 99%

Semantic Face Segmentation Using Convolutional Neural Networks With a Supervised Attention Module

Hizukuri,

Hirata,

Nakayama

2023

IEEE Access

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A self-attention module is often used in image segmentation tasks such as facial part segmentation. Because the self-attention module weights the features at each position using the weighted sum of features at all positions obtained by the middle layer of a convolutional neural network (CNN), the target regions for the segmentation might not be weighted sufficiently. The purpose of this study was to develop a semantic segmentation method for facial parts using a CNN with a supervised attention module that focuses on facial part enhancement. To improve the segmentation accuracy of the facial parts, we propose a new supervised attention module that can enhance features corresponding to pixels with the same class labels on input images and then incorporated it into the CNN. In this study, ResNet-FCN with skip connections was used as the baseline CNN model, and the CelebA Mask HQ dataset was used for the network training and evaluation of the network. The mean intersection over union (IoU) and Dice index for the proposed network were greater than those for ResNet-FCN, SegNet, and PSPNet without the selfattention module, non-local neural networks with the traditional self-attention module, Segformer, and U-Segformer-Hyper with a Transformer. The proposed network achieved a high mean IoU and Dice index, and hence will be useful for segmenting facial parts.

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Emotion Recognition from Facial Expressions Using Type‐2 Fuzzy Sets

Cited by 2 publications

References 54 publications

EmoTunes: Your Emotional Soundtrack

EmoTunes: Your Emotional Soundtrack

Semantic Face Segmentation Using Convolutional Neural Networks With a Supervised Attention Module

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