Robust facial expression recognition system based on hidden Markov models

Elgarrai, Zineb; Meslouhi, Othmane El; Kardouchi, Mustapha; Allali, Hakim

doi:10.1007/s13735-016-0113-8

Cited by 10 publications

(4 citation statements)

References 36 publications

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“…Gabor Wavelets have also been used for the extraction of features related to a facial dataset. Later, Fisher's discriminant based indexing is being utilized to reduce the dimensions of the resultant feature vectors [8]. Ekta Walia et al [9] described the results of some local texture extraction methodologies on Log Gabor filter response.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Multi-resolution and Co-occuring local pattern generator for Image Retrieval

Bhardwaj

Pandove

Dahiya

2019

ICST Transactions on Scalable Information Systems

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Content-based image retrieval (CBIR) is a methodology used to search indistinguishable images across any vast repository. Texture, Color and Shape are among the most prominent features of any CBIR system. Two texture descriptors namely Gray level Co-occurence matrix (GLCM) and Discrete wavelet transform (DWT) have been utilized here for the formation of a hybrid texture descriptor, denoted as (Co-DGLCM). To enhance the retrieval accuracy of the proposed system, a framework of an Extreme learning machine (ELM) with Relevance feedback (RF) has also been used. This technique provides simultaneously spatial relationship and information related to frequency in co-occuring local patterns of an image. Two benchmark texture databases namely Brodatz and MIT-Vistex have been tested and results are obtained in terms of accuracy, total average recall and total average precision which is 96.35% and 97.34% respectively on the two databases.

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Section: Related Workmentioning

confidence: 99%

An Intelligent Multi-resolution and Co-occuring local pattern generator for Image Retrieval

Bhardwaj

Pandove

Dahiya

2019

ICST Transactions on Scalable Information Systems

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“…There are several computer vision-based studies that primarily discuss on aspects such as scene understanding and analysis [ 118 , 148 ], video analysis [ 74 , 129 ], anomaly/abnormality detection methods [ 149 ], human-object detection and tracking [ 36 ], activity recognition [ 121 ], recognition of facial expressions [ 27 ], urban traffic monitoring [ 169 ], human behavior monitoring [ 96 ], detection of unusual events in surveillance scenes [ 82 ], etc. Out of these different aspects, anomaly detection in video surveillance scenes has been discussed further in our review.…”

Section: Introductionmentioning

confidence: 99%

Anomaly detection using edge computing in video surveillance system: review

Patrikar

Parate

2022

Int J Multimed Info Retr

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The current concept of smart cities influences urban planners and researchers to provide modern, secured and sustainable infrastructure and gives a decent quality of life to its residents. To fulfill this need, video surveillance cameras have been deployed to enhance the safety and well-being of the citizens. Despite technical developments in modern science, abnormal event detection in surveillance video systems is challenging and requires exhaustive human efforts. In this paper, we focus on evolution of anomaly detection followed by survey of various methodologies developed to detect anomalies in intelligent video surveillance. Further, we revisit the surveys on anomaly detection in the last decade. We then present a systematic categorization of methodologies for anomaly detection. As the notion of anomaly depends on context, we identify different objects-of-interest and publicly available datasets in anomaly detection. Since anomaly detection is a time-critical application of computer vision, we explore the anomaly detection using edge devices and approaches explicitly designed for them. The confluence of edge computing and anomaly detection for real-time and intelligent surveillance applications is also explored. Further, we discuss the challenges and opportunities involved in anomaly detection using the edge devices.

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“…There are numerous feature extraction techniques and classification techniques developed. SVM is popular classifier for FER systems where as some researchers are using Neural Networks [15], Hidden Markov Models [19,20] and even KNNs [21,22].…”

Section: Introductionmentioning

confidence: 99%

A Facial Expression Recognition Model using Support Vector Machines

Bellamkonda¹,

Gopalan²

2018

IJMSC

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Facial Expression Recognition (FER) has gained interest among researchers due to its inevitable role in the human computer interaction. In this paper, an FER model is proposed using principal component analysis (PCA) as the dimensionality reduction technique, Gabor wavelets and Local binary pattern (LBP) as the feature extraction techniques and support vector machine (SVM) as the classification technique. The experimentation was done on Cohn-Kanade, JAFFE, MMI Facial Expression datasets and real time facial expressions using a webcam. The proposed methods outperform the existing methods surveyed.

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Robust facial expression recognition system based on hidden Markov models

Cited by 10 publications

References 36 publications

An Intelligent Multi-resolution and Co-occuring local pattern generator for Image Retrieval

An Intelligent Multi-resolution and Co-occuring local pattern generator for Image Retrieval

Anomaly detection using edge computing in video surveillance system: review

A Facial Expression Recognition Model using Support Vector Machines

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