Multi-Pose Face Recognition And Tracking System

Nair, Binu M.; Foytik, Jacob; Tompkins, R. Cortland; Diskin, Yakov; Aspiras, Theus; Asari, Vijayan K.

doi:10.1016/j.procs.2011.08.070

Cited by 15 publications

(6 citation statements)

References 4 publications

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“…The feature descriptor algorithms like SIFT, HAAR, HOG takes an image and outputs the feature which encodes the information into a series of numbers and differentiates one feature from another [41,42]. In hatred of the vital advancement in this area, the tracking system has experienced many challenging situations like occlusion, complex motions, fast motion, illumination variation, deformation, image blur, background clutter, scale variation, rotation which debases the general execution of the framework [43,44].…”

Section: Related Workmentioning

confidence: 99%

Robust human tracking using harmonious polling tracker

Wagh

Kanade²

2019

SN Appl. Sci.

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Human tracking is one of the challenging and essential components of an intelligent surveillance system. Variety of correlation filter-based tracking algorithms has presented their brilliance in human tracking. However, few of them have demonstrated disappointments in the presence of occlusion, background clutters, illumination variation, scale variation, fast motion, in-plane rotation, and out of the plane rotation. The paper presents, an improved correlation filter-based tracking algorithm, harmonious polling of patched correlation technique for tracking a human in a video sequence thinking about all the challenging attributes. A human to be tracked is represented by using multiple image patches, as patch-based tracking framework resolves the issues based on occlusion and global scene changes to a great extent. An innovative methodology utilized in the proposed framework is, every individual patch is treated independently thought the process and applied to the polling mechanism, which improves the performance of the system to an extraordinary degree. Kernelized correlation filter is applied to each patch individually generating the correlation score. A polling mechanism is a novel technique used in the proposed framework, which generates the confidence map from the correlation score. The maximum score achieved from the confidence map gives an exact position of the target. The tracker is applied to the number of challenging sequences and contrasted with various outperforming algorithms. The precision and success rate of the proposed tracker is improved by 15% and 19%, respectively. From the qualitative and quantitative analysis, it can be demonstrated that the proposed algorithm beats the cutting-edge execution.

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Section: Related Workmentioning

confidence: 99%

Robust human tracking using harmonious polling tracker

Wagh

Kanade²

2019

SN Appl. Sci.

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“…In the case of deformation methods, the extraction of features relies on shape and texture changes through a period of time. On one hand the Holistic approaches either use the whole face [7,25] or partial information about regions in the face like mouth or eyes [14,21,31]. For the motion extraction methods, the features are extracted by analysing motion vectors: holistically [8,29], and locally [26,34,39] obtained.…”

Section: Computational Perspectivementioning

confidence: 99%

Bio-Inspired Architecture for Clustering into Natural and Non-Natural Facial Expressions

Sánchez¹,

Hernández²,

Orellana³

2012

Visual Cortex - Current Status and Perspectives

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“…To observe behaviour at the cellular level, multiple cellular profiling technologies have analysed gene expression, protein production and sequencing [6][7][8][9]. In parallel, large-scale human population data exhibit similarities in interaction at multiple levels of society that can be, for instance, studied by tracking behaviour patterns of humans in society using face recognition technologies [10], among others. These inferences from cell-cell or human-human interactions can better contribute towards unifying theories that link together intricacies of working principles in life formation, ranging from single cells to complex organisms including humans.…”

Section: Introductionmentioning

confidence: 99%

Cellular sociology regulates the hierarchical spatial patterning and organization of cells in organisms

et al. 2020

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Advances in single-cell biotechnology have increasingly revealed interactions of cells with their surroundings, suggesting a cellular society at the microscale. Similarities between cells and humans across multiple hierarchical levels have quantitative inference potential for reaching insights about phenotypic interactions that lead to morphological forms across multiple scales of cellular organization, namely cells, tissues and organs. Here, the functional and structural comparisons between how cells and individuals fundamentally socialize to give rise to the spatial organization are investigated. Integrative experimental cell interaction assays and computational predictive methods shape the understanding of societal perspective in the determination of the cellular interactions that create spatially coordinated forms in biological systems. Emerging quantifiable models from a simpler biological microworld such as bacterial interactions and single-cell organisms are explored, providing a route to model spatio-temporal patterning of morphological structures in humans. This analogical reasoning framework sheds light on structural patterning principles as a result of biological interactions across the cellular scale and up.

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Multi-Pose Face Recognition And Tracking System

Cited by 15 publications

References 4 publications

Robust human tracking using harmonious polling tracker

Robust human tracking using harmonious polling tracker

Bio-Inspired Architecture for Clustering into Natural and Non-Natural Facial Expressions

Cellular sociology regulates the hierarchical spatial patterning and organization of cells in organisms

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