Facial expression detection

Matre, G. N.; Shah, Shail Kumari

doi:10.1109/iccic.2013.6724242

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…Using the features extracted from faces as the representation of facial expressions is a common way [6]. For decades of research, many kinds of features have been derived, such as HOG, SIFT, SURF, Wavelet, Gabor, DCT and etc.…”

Section: Introduction and Realted Workmentioning

confidence: 99%

In-the-wild facial expression recognition in extreme poses

Yang

Zhang

Zheng³

et al. 2018

Ninth International Conference on Graphic and Image Processing (ICGIP 2017)

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In the computer research area, facial expression recognition is a hot research problem. Recent years, the research has moved from the lab environment to in-the-wild circumstances. It is challenging, especially under extreme poses. But current expression detection systems are trying to avoid the pose effects and gain the general applicable ability. In this work, we solve the problem in the opposite approach. We consider the head poses and detect the expressions within special head poses. Our work includes two parts: detect the head pose and group it into one pre-defined head pose class; do facial expression recognize within each pose class. Our experiments show that the recognition results with pose class grouping are much better than that of direct recognition without considering poses. We combine the hand-crafted features, SIFT, LBP and geometric feature, with deep learning feature as the representation of the expressions. The handcrafted features are added into the deep learning framework along with the high level deep learning features. As a comparison, we implement SVM and random forest to as the prediction models. To train and test our methodology, we labeled the face dataset with 6 basic expressions.

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Section: Introduction and Realted Workmentioning

confidence: 99%

In-the-wild facial expression recognition in extreme poses

Yang

Zhang

Zheng³

et al. 2018

Ninth International Conference on Graphic and Image Processing (ICGIP 2017)

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“…There are so many methods that successful to detect face from the image, but Face detection suffers from the conditions such as Pose variation, Feature occlusion, Facial expression, imaging conditions. Face detection methods can be divided into four types: Knowledge-Based Method, Feature-Invariant Methods, Template Matching Methods, Appearance-Based Methods [11] [12].…”

Section: Introductionmentioning

confidence: 99%

“…Feature extraction is the process of extracting relevant information from a face image [12]. The output of the feature extractor stage must contain separable and classifiable vectors.…”

Section: Introductionmentioning

confidence: 99%

Facial Behavior Recognition Using Soft Computing Techniques: A Survey

Kantharia¹,

Prajapati²

2015

2015 Fifth International Conference on Advanced Computing &Amp; Communication Technologies

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Human facial behaviour recognition can be defined as the process of identifying human internal feelings or mood from the classification of facial expression and gesture. Human facial expression and gesture recognition have a many real world applications such as Human Machine Intelligent Interaction(HMII), Smart rooms, Advance Driver Assistance Systems(ADAS), Intelligent Robotics, Monitoring and Surveillance, Gaming, Research on pain and depression, Health support appliances. Facial Expression Recognition is challenging problem up till now because of many reasons, moreover, it consists of three sub challenging tasks face detection, facial feature extraction and expression classification. Soft computing is a computer science field that applies to the problem whose solution is unpredictable or inexact. Digital image processing works effectively together with soft computing techniques to improve efficiency of recognizing human facial behaviour through machine. This paper gives a review on the mechanisms of human facial behavior recognition using soft computing techniques, which includes a brief detail on framework, literature survey and key issues in facial behaviour recognition using soft computing.

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“…The proposed study uses the Log Gabor lter and Gabor lter for feature mining followed by the implementation of an SVM classi er for training and classi cation purposes. Matre & Shah[43] Gives a brief of different techniques used in facial expression detection with their advantages and disadvantages. The author mentioned that, we can do facial expression recognition [26], Local Directional Patterns (LDA)[27], and Fisher Face Method are, consider feature-based extraction methods.…”

mentioning

confidence: 99%

Database Development and Recognition of Facial Expression using Deep Learning

Bapat

Patil

Mali

2023

Preprint

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Facial expressions reflect human emotions and an individual's intentions. To detect facial expressions by human beings is a very easy task whereas it’s a very difficult task using computers. They perform a vigorous part in everyday life. It is a non-verbal mode that may include feelings, opinions, and thoughts without speaking. Deep neural networks, Convolutional Neural Networks, Neural networks, Artificial Intelligence, Fuzzy Logic, and Machine Learning are the different technologies used to detect facial expressions. To detect facial expressions, static images, video, webcam data, or real-time images can be used. This research paper focused on developing the SMM Facial Expression dataset and proposes a convolutional neural network model to identify facial expressions. The proposed method was tested on two different benchmarked datasets namely FER2013 and CK+ for facial expression detection. We have explored the proposed model on CK+ and achieved 93.94% accuracy and 67.18 % for FER2013 respectively. To analyze and test the accuracy of the proposed model, we have implemented it on the SMM Facial Expression dataset and achieved 96.60% of accuracy.

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Facial expression detection

Cited by 8 publications

References 9 publications

In-the-wild facial expression recognition in extreme poses

In-the-wild facial expression recognition in extreme poses

Facial Behavior Recognition Using Soft Computing Techniques: A Survey

Database Development and Recognition of Facial Expression using Deep Learning

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