A survey on facial feature extraction techniques for automatic face annotation

Patel, Twisha S; Shah, Bhumika

doi:10.1109/icimia.2017.7975607

Cited by 9 publications

(3 citation statements)

References 15 publications

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“…The main motto of this work is to take and manage attendance using face recognition. The testing of this technique has proceeded through Raspberry Pi devices [1].…”

Section: Introductionmentioning

confidence: 99%

Face Detection and Recognition Using Raspberry Pi

Dolas

Ghogare

Kshirsagar

et al. 2021

Techno-Societal 2020

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In Today's world, security frames the most essential segment of our lives. Face Recognition is an important part of the purpose of the security and surveillance field. A small project which does face detection using OpenCV library on Raspberry Pi. Face Recognition/Facial Recognition is a category of biometric software that identifies people by their faces. The face is captured by the digital camera and the system is trained and then it is capable of identifying the person. This paper focuses on the implementation of a face detection system for human identification based on the open-source computer vision library (OpenCV) with python. We also proposed a hierarchical image processing approach to reduce the training or testing time while improving recognition accuracy.

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“…The main motto of this work is to take and manage attendance using face recognition. The testing of this technique has proceeded through Raspberry Pi devices [1].…”

Section: Introductionmentioning

confidence: 99%

Face Detection and Recognition Using Raspberry Pi

Dolas

Ghogare

Kshirsagar

et al. 2021

Techno-Societal 2020

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show abstract

“…Facial expression shows the mood or emotional state of an individual how he is feeling at a particular moment like sad, happy, anger. Twisha Patel [1] investigates about various facial feature extraction techniques which is good review. [2] gives the various algorithms and their link with the facial expression detection.…”

Section: Introductionmentioning

confidence: 99%

Differentiating Trauma from Normal Human Emotion using Recurrent Neural Network

Sofia*¹,

Sivakumar²

2019

IJRTE

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“…SVM is often reported to achieve better results than other classifiers. [6,10,11] Another classification method is the nearest neighbor (NN) algorithm. [12][13][14] According to NN, one set of samples is handled such that these samples can be classified by minimizing the distance among them.…”

mentioning

confidence: 99%

Exploiting Wavelet Transform, Principal Component Analysis, Support Vector Machine, and K-Nearest Neighbors for Partial Face Recognition

Alhabib

Al-Dabagh²,

Almukhtar

et al. 2019

Cihan U Erbil SCI J

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Facial analysis has evolved to be a process of considerable importance due to its consequence on the safety and security, either individually or generally on the society level, especially in personal identification. The paper in hand applies facial identification on a facial image dataset by examining partial facial images before allocating a set of distinctive characteristics to them. Extracting the desired features from the input image is achieved by means of wavelet transform. Principal component analysis is used for feature selection, which specifies several aspects in the input image; these features are fed to two stages of classification using a support vector machine and K-nearest neighborhood to classify the face. The images used to test the strength of the suggested method are taken from the well-known (Yale) database. Test results showed the eligibility of the system when it comes to identify images and assign the correct face and name.

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A survey on facial feature extraction techniques for automatic face annotation

Cited by 9 publications

References 15 publications

Face Detection and Recognition Using Raspberry Pi

Face Detection and Recognition Using Raspberry Pi

Differentiating Trauma from Normal Human Emotion using Recurrent Neural Network

Exploiting Wavelet Transform, Principal Component Analysis, Support Vector Machine, and K-Nearest Neighbors for Partial Face Recognition

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