Facial expression recognition using bezier curves with hausdorff distance

Babu, D. R. Ramesh; Shankar, Ravi; Mahesh, G; Murthy, K. V. S.

doi:10.1109/iciota.2017.8073622

Cited by 15 publications

(11 citation statements)

References 8 publications

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“…An Image Extractor technique based on a genetic algorithm [Shankar et al (2017)] was used to increase picture retrieval performance. They used NN-based Decision Trees with 80% accuracy to retrieve face expressions using emotions by drawing Beizer curves [Babu et al (2017)]. They used XML terms to acquire diverse responses from query keywords within the data to extract information [Rajanikanth et al (2016)].…”

Section: Performmentioning

confidence: 99%

An Edge Clustered Segmentation Based Model for Precise Image Retrieval

Devareddi¹,

A.²

2022

INDJCSE

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The modern era necessitates efficient smart image retrieval from various image collections. Image retrieval relies heavily on primitive image signatures and their internal features. Image retrieval relies heavily on deep metric learning, which aims to identify semantic similarities between data points in the image for accurate image retrieval procedures. The image shape feature representation was generated using a histogram image processing model. To limit the search space, the image pixel shape-based retrieval procedures are effectively used for image retrieval. The dominant colour, edge and shape descriptor has become a common feature in image processing applications. Because of lighting and other variables, colour in nature can shift slightly. A consistent region of an image is detected and extracted from this consistent zone for an accurate image retrieval strategy by performing image segmentation. The proposed model implements a Related Edge Clustered Pixel Extraction Model with Weighted Feature Vector Set (RECPE-WFVS) for extracting the image content set for searching with the query image for an accurate image retrieval procedure. The proposed model is compared with the traditional Remote Sensing Image Retrieval approach based on Fully Convolutional Network (RSIR-FCN) and Classification Using High-Resolution Remote Sensing Images (CHR-RSI), and the results represent that the proposed model's performance is high.

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Section: Performmentioning

confidence: 99%

An Edge Clustered Segmentation Based Model for Precise Image Retrieval

Devareddi¹,

A.²

2022

INDJCSE

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“…A picture that has two potential qualities for every pixel is known as a paired picture. Commonly, the two hues utilized for a twofold picture are high contrast though any two hues can be utilized [6]. The shading used for the object(s) in the picture is the frontal area shading while the rest of the picture is the foundation shading [3].…”

Section: Binary Conversion Of the Imagementioning

confidence: 99%

“…A Bezier twist could be a parametric curve as regularly as conceivable utilized in PC representations and related fields. Moreover Bezier curves are used in the time territory, mostly in animation and interface plan [6]. Bezier curves with their control points is shown in Figure 4.…”

Section: Bezier Curves Detection [6]mentioning

confidence: 99%

“…The Bezier curve [6] individual coordinates are characterized by pair of parametric equations given below: Figure 4: Bezier curves and their control points.…”

Section: Bezier Curves Detection [6]mentioning

confidence: 99%

“…We first standardize moves that change over each width of the Bezier bend to 100 and its tallness according to its width to determine a co-ordination of similarity. We use the Hausdorff separation at this stage to look at the metric structure of them [6]…”

Section: Facial Emotion With Hausdorff Distance [6]mentioning

confidence: 99%

See 2 more Smart Citations

Facial Expression Recognition System

2020

International Journal For Innovative Engineering and Management

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Emotion recognition is a prominent tough problem in machine vision systems. The significant way humans show emotions is through facial expressions. In this paper we used a 2D image processing method to recognize the facial expression by extracting of features. The proposed algorithm passes through few preprocessing steps initially. And then the preprocessed image is partitioned into two main parts Eyes and Mouth. To identify the emotions Bezier curves are drawn for main parts. The experimental result shows that the proposed technique is 80% to 85% accurate.

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Analysis of Biometric Identification Methods Used in Auto-Proctoring

Bekenova,

Gumarova

et al. 2024

Springer Geography

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Facial expression recognition using bezier curves with hausdorff distance

Cited by 15 publications

References 8 publications

An Edge Clustered Segmentation Based Model for Precise Image Retrieval

An Edge Clustered Segmentation Based Model for Precise Image Retrieval

Facial Expression Recognition System

Analysis of Biometric Identification Methods Used in Auto-Proctoring

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