Guneet Bhullar - Academia.edu (original) (raw)
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Université des Sciences et Technologies de Lille (Lille-1)
Université des Sciences et Technologie Houari Boumediene (USTHB)
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Papers by Guneet Bhullar
International Journal of Computer Applications, 2016
The process of Face Recognition comprises of Face Detection, feature extraction and verification ... more The process of Face Recognition comprises of Face Detection, feature extraction and verification or identification. The extraction and identification are stages in the FR process. Many face recognition algorithms have been developed. This has resulted in development of manifold robust techniques such as background removal, illumination normalization and others which support the algorithm to withstand the undesirable effects and improve the success rate. This paper provides a survey and method for face pose estimation. This method is based on feature extraction points of two different face poses and then matched points between these two face poses will give the results. This method is one of the simplest methods for low resolution images.
Face pose estimation is an issue in many vision systems such as face-based bio-metrics, scene und... more Face pose estimation is an issue in many vision systems such as face-based bio-metrics, scene understanding for humans, and others. It is a robust approach that performs facial pose estimation by examining the behaviour of key facial features over a wide range of poses. Such methods are useful in intelligent vision systems for entertainment, human computer interaction, and security. The methods used for face pose estimation are based on different features such as mutual information which depends on information contained in two video frames. In another method, active shape models are used in symmetrical feature model for extraction of feature of faces. Synergetic computer approach is another approach for estimation of facial pose. In this, modified basic synergetic computer such as SC-MELT (Synergetic Computers with Melting) are used.. The methods based on dense reconstruction and sparse representations are also important. Sparse representation of input signal can be determined by a linear combination of sparse subset of the bases. Dense reconstruction is a linear regression model for the training samples.
International Journal of Computer Applications, 2016
The process of Face Recognition comprises of Face Detection, feature extraction and verification ... more The process of Face Recognition comprises of Face Detection, feature extraction and verification or identification. The extraction and identification are stages in the FR process. Many face recognition algorithms have been developed. This has resulted in development of manifold robust techniques such as background removal, illumination normalization and others which support the algorithm to withstand the undesirable effects and improve the success rate. This paper provides a survey and method for face pose estimation. This method is based on feature extraction points of two different face poses and then matched points between these two face poses will give the results. This method is one of the simplest methods for low resolution images.
Face pose estimation is an issue in many vision systems such as face-based bio-metrics, scene und... more Face pose estimation is an issue in many vision systems such as face-based bio-metrics, scene understanding for humans, and others. It is a robust approach that performs facial pose estimation by examining the behaviour of key facial features over a wide range of poses. Such methods are useful in intelligent vision systems for entertainment, human computer interaction, and security. The methods used for face pose estimation are based on different features such as mutual information which depends on information contained in two video frames. In another method, active shape models are used in symmetrical feature model for extraction of feature of faces. Synergetic computer approach is another approach for estimation of facial pose. In this, modified basic synergetic computer such as SC-MELT (Synergetic Computers with Melting) are used.. The methods based on dense reconstruction and sparse representations are also important. Sparse representation of input signal can be determined by a linear combination of sparse subset of the bases. Dense reconstruction is a linear regression model for the training samples.