Facial Expression Recognition with Occlusions Based on Geometric Representation

Cornejo, Jadisha Yarif Ramírez; Pedrini, Hélio; Flórez-Revuelta, Francisco

doi:10.1007/978-3-319-25751-8_32

Cited by 18 publications

(15 citation statements)

References 10 publications

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“…If the classifier performance is inadequate then the system is retrained till the optimum result will reach. The neural network classifiers, SVM, and Bayesian classifiers are some of the classifiers used for this purpose [31,32].…”

Section: Classificationmentioning

confidence: 99%

Down syndrome identification and classification using facial features with neural network

VK¹,

Rajesh²

2022

Global J. Eng. Technol. Adv.

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The medical image tool named Ultrasound imaging is used for the analysis of fetus behaviour. Due to the presence of noise the quality of these images is low. Proper filtering is required to suppress this noise. Before the fatal development analysis, it is required image processing method. The processed images are then used for the analysis and it helps to take care of the health. Down syndrome is one of the crucial chromosomal disorders. It is due to the presence of a further copy of chromosome 21. The ultrasound imaging helps to point out DS in the earlier stage of pregnancy by handling the image in an efficient manner. In this work, nasal bone identification and texturing methods are used to detect the disease. Here we designed a nasal bone identification module for the image classification as normal or abnormal. Down syndrome detection utilizes a collection of facial expression images. A compact geometric descriptor is employed for extracting the facial features from the image set. There is no specific treatment for Down syndrome. Thus, early detection and screening of this disability are the best styles for Down syndrome prevention.

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Section: Classificationmentioning

confidence: 99%

Down syndrome identification and classification using facial features with neural network

VK¹,

Rajesh²

2022

Global J. Eng. Technol. Adv.

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“…For example, an angry pedestrian might be more likely to behave more assertively in crossing the road in front of an AV. Cornejo et al [56], [55] developed a facial expression recognition method that is robust to occlusions. The occluded facial expression is reconstructed with a robust principal component analysis (PCA) method, facial features are extracted using Gabor wavelets and geometric features in [56] and using CENTRIST features in [55], recognition is performed with KNN and SVM as classifiers.…”

Section: Emotion Recognitionmentioning

confidence: 99%

“…Cornejo et al [56], [55] developed a facial expression recognition method that is robust to occlusions. The occluded facial expression is reconstructed with a robust principal component analysis (PCA) method, facial features are extracted using Gabor wavelets and geometric features in [56] and using CENTRIST features in [55], recognition is performed with KNN and SVM as classifiers. Cambria et al [32] proposed a new categorization model for emotion recognition systems and [31] reviewed sentiment analysis methods.…”

Section: Emotion Recognitionmentioning

confidence: 99%

Pedestrian Models for Autonomous Driving Part I: Low-Level Models, From Sensing to Tracking

Camara

Bellotto

Coşar

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets. Unlike static obstacles, pedestrians are active agents with complex, interactive motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behaviour as well as detecting and tracking them. This narrative review article is Part I of a pair, together surveying the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from low-level image detection to high-level psychology models, from the perspective of an AV designer. This self-contained Part I covers the lower levels of this stack, from sensing, through detection and recognition, up to tracking of pedestrians. Technologies at these levels are found to be mature and available as foundations for use in high-level systems, such as behaviour modelling, prediction and interaction control.

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“…The method produced 5% and 16% higher accuracies for hands and sunglasses occlusion respectively than using Adaboost alone on the JAFFE and BHUFE databases. Rather than using Haar-like features, Cornejo et al [2015] extracted Gabor wavelets and a geometric representation of 22 facial points from the recovered regions of occlusion using RPCA. A KNN or SVM classifier was adopted for recognizing expressions in the presence of five types of occlusion, including two eyes, left eye, right eye, bottom left or bottom right part of the face, and bottom part of the face.…”

Section: Texture Based Approachmentioning

confidence: 99%

Facial Expression Analysis under Partial Occlusion

et al. 2018

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Automatic machine-based Facial Expression Analysis (FEA) has made substantial progress in the past few decades driven by its importance for applications in psychology, security, health, entertainment and human computer interaction. The vast majority of completed FEA studies are based on non-occluded faces collected in a controlled laboratory environment. Automatic expression recognition tolerant to partial occlusion remains less understood, particularly in real-world scenarios. In recent years, efforts investigating techniques to handle partial occlusion for FEA have seen an increase. The context is right for a comprehensive perspective of these developments and the state of the art from this perspective. This survey provides such a comprehensive review of recent advances in dataset creation, algorithm development, and investigations of the effects of occlusion critical for robust performance in FEA systems. It outlines existing challenges in overcoming partial occlusion and discusses possible opportunities in advancing the technology. To the best of our knowledge, it is the first FEA survey dedicated to occlusion and aimed at promoting better informed and benchmarked future work.

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Facial Expression Recognition with Occlusions Based on Geometric Representation

Cited by 18 publications

References 10 publications

Down syndrome identification and classification using facial features with neural network

Down syndrome identification and classification using facial features with neural network

Pedestrian Models for Autonomous Driving Part I: Low-Level Models, From Sensing to Tracking

Facial Expression Analysis under Partial Occlusion

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