Face recognition techniques, their advantages, disadvantages and performance evaluation

Masupha, Lerato; Zuva, Tranos; Ngwira, Seleman M.; Esan, Omobayo A.

doi:10.1109/cccs.2015.7374154

Cited by 32 publications

(15 citation statements)

References 6 publications

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“…Face recognition is a technique that is used to recognise a person from his/her face that has been previously trained from a dataset [10]. Face recognition is one of the most efficient biometric techniques to identify a person [11], and has advantages compared to other biometric methods, such as identifying could be done without requiring action from the user, it has non-intrusive characteristics [12]. Face recognition is a technology that can be applied in various fields, such as surveillance, smart cards, entertainment, law enforcement, information security, image database investigation, civilian applications, human-computer interactions [13].…”

Section: B Face Recognitionmentioning

confidence: 99%

“…Face recognition itself can be divided based on its approach. Those approaches are the feature-based approach, holistic-based approach, and hybrid-based approach [12], [14]. All three are explained below.…”

Section: B Face Recognitionmentioning

confidence: 99%

“…A feature-based approach to input image processing to identify and extract unique features in the face, such as eyes, mouth, nose and so on, then calculating the geometric relationship between the points of the face, so that the input face image is converted into a geometric feature vector [12]. Feature-based itself is divided into Geometric feature-based matching or template based, and elastic bunch graph.…”

Section: ) Feature-based Approachmentioning

confidence: 99%

“…Elastic bunch graph is a technique based on dynamic link structures. For each face, a graph is generated by using fiducial points, with each fiducial point represents a node of a fully connected graph, and labelled using Gabor filter response [12].…”

Section: ) Feature-based Approachmentioning

confidence: 99%

See 3 more Smart Citations

Automatic Attendance System for University Student Using Face Recognition Based on Deep Learning

Sutabri¹,

Pamungkur²,

Kurniawan³

et al. 2019

IJMLC

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Student attendance is essential in the learning process. To record student attendance, several ways can be done; one of them is through student signatures. The process has several shortcomings, such as requiring a long time to make attendance; the attendance paper is lost, the administration must enter attendance data one by one into the computer. To overcome this, the paper proposed a web-based student attendance system that uses face recognition. In the proposed system, Convolutional Neural Network (CNN) is used to detect faces in images, deep metric learning is used to produce facial embedding, and K-NN is used to classify student's faces. Thus, the computer can recognize faces. From the experiments conducted, the system was able to recognize the faces of students who did attend and their attendance data was automatically saved. Thus, the university administration is alleviated in recording attendance data.

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Section: B Face Recognitionmentioning

confidence: 99%

Section: B Face Recognitionmentioning

confidence: 99%

Section: ) Feature-based Approachmentioning

confidence: 99%

Section: ) Feature-based Approachmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Attendance System for University Student Using Face Recognition Based on Deep Learning

Sutabri¹,

Pamungkur²,

Kurniawan³

et al. 2019

IJMLC

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“…These types of difficulties appear because faces need to be represented in such a way that best utilizes the available face information to define a specific face from all the other faces in the database. Face pose is a specifically difficult problem in this aspect simply because all faces seem similar; specifically, all faces consist of two eyes, mouth, nose, and other features that are in the same location [9].…”

Section: Introductionmentioning

confidence: 99%

Face Recognition Using Wavelet Neural Network

Dharani¹,

Vibhute²

2017

IJARCSSE

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Automatic face recognition system is an important component of intelligent human computer interaction systems for biometric. It is an attractive biometric approach, to distinguish one person from another. To perform Automatic face recognition system, the hybrid approach Wavelets face detection and Neural Network based Face Recognition is used. The face recognition accuracy is can be increased using a combination of Wavelet, PCA-FLD and Neural Networks. Preprocessing, feature extraction and classification rules are three crucial issues for face recognition. For preprocessing and feature extraction steps, we apply a combination of wavelet transform and PCA-FLD. During the classification stage, the Neural Network is explored to achieve a robust decision in presence of wide facial variations.

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Digital technologies to assess yoghurt quality traits and consumers acceptability

et al. 2022

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BACKGROUND: Sensory biometrics provide advantages for consumer tasting by quantifying physiological changes and the emotional response from participants, removing variability associated with self-reported responses. The present study aimed to measure consumers' emotional and physiological responses towards different commercial yoghurts, including dairy and plant-based yoghurts. The physiochemical properties of these products were also measured and linked with consumer responses.RESULTS: Six samples (Control, Coconut, Soy, Berry, Cookies and Drinkable) were evaluated for overall liking by n = 62 consumers using a nine-point hedonic scale. Videos from participants were recorded using the Bio-Sensory application during tasting to assess emotions and heart rate. Physicochemical parameters Brix, pH, density, color (L, a and b), firmness and nearinfrared (NIR) spectroscopy were also measured. Principal component analysis and a correlation matrix were used to assess relationships between the measured parameters. Heart rate was positively related to firmness, yaw head movement and overall liking, which were further associated with the Cookies sample. Two machine learning regression models were developed using (i) NIR absorbance values as inputs to predict the physicochemical parameters (Model 1) and (ii) the outputs from Model 1 as inputs to predict consumers overall liking (Model 2). Both models presented very high accuracy (Model 1: R = 0.98; Model 2: R = 0.99). CONCLUSION:The presented methods were shown to be highly accurate and reliable with respect to their potential use by the industry to assess yoghurt quality traits and acceptability.

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Face recognition techniques, their advantages, disadvantages and performance evaluation

Cited by 32 publications

References 6 publications

Automatic Attendance System for University Student Using Face Recognition Based on Deep Learning

Automatic Attendance System for University Student Using Face Recognition Based on Deep Learning

Face Recognition Using Wavelet Neural Network

Digital technologies to assess yoghurt quality traits and consumers acceptability

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