Recognition of Faces – An Optimized Algorithmic Chain

Shankar, Sheela; Udupi, V. R.

doi:10.1016/j.procs.2016.06.020

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…Face recognition using an optimized algorithm chain for both 2D and 3D images gives an accuracy about 96% with SVM classifier using LBP and PCA. Further testing on 2D and 3D images using LBP and PCA with FFBPNN (Feed Forward Back Propagation Neural Network) is less effective and efficient as compared to the SVM classifier [8]. Locality Preserving Projections (LPPs) have been used for manifold systems originated from Local Binary Pattern (LBP) subjects [9].…”

Section: Literature Survey and Theoretical Frameworkmentioning

confidence: 99%

An Effective Combination of Textures and Wavelet Features for Facial Expression Recognition

Hassan

Alghamdi

Hafeez

et al. 2021

Eng. Technol. Appl. Sci. Res.

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In order to explore the accompanying examination goals for facial expression recognition, a proper combination of classification and adequate feature extraction is necessary. If inadequate features are used, even the best classifier could fail to achieve accurate recognition. In this paper, a new fusion technique for human facial expression recognition is used to accurately recognize human facial expressions. A combination of Discrete Wavelet Features (DWT), Local Binary Pattern (LBP), and Histogram of Gradients (HoG) feature extraction techniques was used to investigate six human emotions. K-Nearest Neighbors (KNN), Decision Tree (DT), Multi-Layer Perceptron (MLP), and Random Forest (RF) were chosen for classification. These algorithms were implemented and tested on the Static Facial Expression in Wild (SWEW) dataset which consists of facial expressions of high accuracy. The proposed algorithm exhibited 87% accuracy which is higher than the accuracy of the individual algorithms.

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Section: Literature Survey and Theoretical Frameworkmentioning

confidence: 99%

An Effective Combination of Textures and Wavelet Features for Facial Expression Recognition

Hassan

Alghamdi

Hafeez

et al. 2021

Eng. Technol. Appl. Sci. Res.

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“…Reflections can also prove detrimental to the system's facial recognition abilities. To improve this system, the system should be able to accurately distinguish a human face from patterns of dirt, print, or illumination that resemble a human face [28]. To accomplish this, a better method of facial recognition should be employed.…”

Section: Recommendationsmentioning

confidence: 99%

Face Recognition Using MATLAB

Africa¹,

Abello²,

Gacuya³

et al. 2019

IJATCSE

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This project dives into the topic of facial recognition and facial detection in a digital communications system. Face recognition is a technology that is widely used today which brings various benefits to society. Facial recognition differs from facial detection in the aspect that facial detection only finds and detects the present face/s in an image whereas, in facial recognition, the computer finds the face/s present in a subject and is able to distinguish the face from a sample of different faces. This research focuses on the implementation of both a facial recognition system and a facial detection system in MATLAB. This research would use the different imaging toolboxes available in the program and would be judged on its ability to accurately detect and recognize a sample in a given database. Additionally, this system should be able to create and to read a database of different faces.

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An efficient text-independent speaker verification for short utterance data from Mobile devices

Arora¹,

Vig

2019

Multimed Tools Appl

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Recognition of Faces – An Optimized Algorithmic Chain

Cited by 5 publications

References 15 publications

An Effective Combination of Textures and Wavelet Features for Facial Expression Recognition

An Effective Combination of Textures and Wavelet Features for Facial Expression Recognition

Face Recognition Using MATLAB

An efficient text-independent speaker verification for short utterance data from Mobile devices

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