A Study of Local Binary Pattern Method for Facial Expression Detection

Bhatt, Drashti H.; Rathod, Kirit R.; Agravat, Shardul J.

doi:10.14445/22312803/ijctt-v7p143

Cited by 7 publications

(6 citation statements)

References 7 publications

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“…Zhang et al investigated two types of features, the geometry-based features and Gabor wavelets based features, for facial expression recognition. Appearance based methods, feature invariant methods, knowledge based methods, Template based methods are the face detection strategies whereas Local Binary Pattern phase correlation, Haar classifier, AdaBoost, Gabor Wavelet are the expression detection strategies in related field [3]. Face reader is the premier for automatic analysis of facial expression recognition and Emotient, Affectiva, Karios etc are some of the API's for expression recognition.…”

Section: Related Workmentioning

confidence: 99%

“…Facial feature representation is to extract a set of appropriate features from original face images for describing faces. Histogram of Oriented Gradient (HOG), SIFT, Gabbor Fitters and Local Binary Pattern (LBP) are the algorithms used for facial feature representation [3,4]. LBP is a simple yet very efficient texture operator which labels the pixels of an image by thresholding the neighborhood of each pixel and considers the result as a binary number.…”

Section: Related Workmentioning

confidence: 99%

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Expression Recognition using Neural Network

Prasad¹

2019

IJRASET

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A facial expression is a gesture created by the facial muscles that can convey human emotions. Facial expression recognition system is used to recognize different emotions of a face. A facial expression is exhibited by the movement of muscles underneath the face skin. Facial expression Recognition comprises of three main phases viz. face detection using Haar classifier, feature Selection using Local Binary Pattern algorithm and Expression Classification using Support Vector Machines. The image files for training are stored in the form of pixels in CSV file and are then converted by the algorithm to form images which subsequently decreases the size of the training data set. The proposed system predicts various emotions from a single image rather than predicting only one kind of emotion as done by the existing systems. Training of the system is done using the neural network.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Expression Recognition using Neural Network

Prasad¹

2019

IJRASET

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“…As illustrated in Fig. 3, each pixel is compared with its eight neighbour in a 3 × 3 neighborhood by subtracting the center pixel value [5] [6]. The resulting strictly negative values are encoded with 0, and the others with 1.…”

Section: ) Geometric and Grayscale Featuresmentioning

confidence: 99%

Multi features combination for automated zooplankton classification

Wang

Dai

Zheng

et al. 2016

OCEANS 2016 - Shanghai

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Zooplankton are the key components of marine food webs. The abundance of it influences the ocean ecological balance. To efficiently monitor species richness of zooplankton and protect marine environment, marine biologists and computer vision experts started to research automated zooplankton classification system with computer vision technologies. Most current research focuses on achieving high classification accuracy. In this paper, we propose a new system based on multi features combination to enhance the zooplankton classification performance. In our system, the geometric and grayscale features, Local Binary Patterns features, and Inner-distance Shape Context features are extracted as low-level features. According to the properties of machine learning algorithms, an appropriate algorithm is chosen to generate middle-level features by processing all kinds of lowlevel features. After that, we concatenate middle-level features and apply Support Vector Machine to get the final classifier. By combining different types of features, the system we proposed can capture richer biomorphic information than those with a few features. And the experimental results also show that our system achieves better classification performance.

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“…LOCAL BINARY PA Local binary patterns [9] feat algorithm is widely used due to its in giving the features once trhe algo to an image. It is a texture pa algorithm which has been proved to both face recognition & fac recognition.…”

Section: IImentioning

confidence: 99%

Effective face detection, feature extraction & neural network based approaches for facial expression recognition

Muttu

Virani

2015

2015 International Conference on Information Processing (ICIP)

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Face expression recognition is a typical task to make human and machine interaction possible. Besides this, medical science and other applications demand for such system. This paper focusses on importance of face detection and its feature parts. For this, Viola -Jones algorithm was implemented. The crucial part of this paper is feature extraction and the algorithm used for the purpose is modified local binary patterns algorithm. The results of feature extraction algorithms are compared to that in the literature to show the limitations of the existing algorithms and their suitabilility for this application. Neural network based approaches are used for classification of facial expressions. This paper enumerates two classification techniques for the facial expressions. The system performs well by the method used by us and gives efficient results. This was experimented using the Taiwanian database and the Japanese database.

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A Study of Local Binary Pattern Method for Facial Expression Detection

Cited by 7 publications

References 7 publications

Expression Recognition using Neural Network

Expression Recognition using Neural Network

Multi features combination for automated zooplankton classification

Effective face detection, feature extraction & neural network based approaches for facial expression recognition

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