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  1. 1
    Facial Feature Extraction Based on Improved Harris Corner Detection Algorithm

    Facial Feature Extraction Based on Improved Harris Corner Detection Algorithm by Bagherian, Elhaam

    Published 2011
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  2. 2
    Facial features point localization using modified SIFT scale space / Zulfikri Paidi

    Facial features point localization using modified SIFT scale space / Zulfikri Paidi by Paidi, Zulfikri

    Published 2020
    “…Surface change creates high-dimensional data during feature extraction work. There are many algorithms proposed for recognition of facial expressions, including SIFT algorithms that are considered superior in performing feature extraction. …”
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  3. 3
    Object-Oriented Programming semantics representation utilizing agents

    Object-Oriented Programming semantics representation utilizing agents by Mohd Aris, Teh Noranis

    Published 2011
    “…A formal algorithm that can be applied to any two related Java-based source codes examples is invented to generate the semantics of these source codes. …”
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  4. 4
    3D face registration across pose variation and facial expression using cross profile alignment

    3D face registration across pose variation and facial expression using cross profile alignment by Anuar, Laili Hayati

    Published 2011
    “…Thus, as the first step prior face registration, the thesis proposed a novel nose tip region detection algorithm using localized point signature, developed specially to locate the nose tip region across various facial variation. …”
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  5. 5
    Automatic detection of face and facial features

    Automatic detection of face and facial features by Chai, Tong Yuen, Mohd Rizon, Mohamed Juhari, Prof. Dr., Woo, San San, Sugisaka, M.

    Published 2011
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  6. 6
    A new descriptor for smile classification based on cascade classifier in unconstrained scenarios

    A new descriptor for smile classification based on cascade classifier in unconstrained scenarios by Hassen, Oday Ali, Abu, Nur Azman, Zainal Abidin, Zaheera, Saad, Mohamed Darwish

    Published 2021
    “…In this paper, an adaptive model for smile classification is suggested that integrates a row-transform-based feature extraction algorithm and a cascade classifier to increase the precision of facial recognition. …”
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  7. 7
    Extracting feature from images by using K-Means clustering algorithm / Abdul Hakim Zainal Abidin

    Extracting feature from images by using K-Means clustering algorithm / Abdul Hakim Zainal Abidin by Zainal Abidin, Abdul Hakim

    Published 2016
    “…This research scope are to develop a computer application that can extract meaningful information in images by implement KMeans clustering algorithm 10 self capture facial image will be use as the research subject to test the algorithm that will extracting meaningful information of the person. …”
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  8. 8
    Feature extraction and localisation using scale-invariant feature transform on 2.5D image

    Feature extraction and localisation using scale-invariant feature transform on 2.5D image by Suk, Ting Pui, Jacey-Lynn, Minoi, Terrin, Lim, Fradinho Oliveira, João, Fyfe Gillies, Duncan

    Published 2015
    “…Hence, we aim to develop methods to automate as much as possible the process of landmarking facial features. …”
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  9. 9
    Facial age range estimation using geometric ratios and hessian-based filter wrinkle analysis

    Facial age range estimation using geometric ratios and hessian-based filter wrinkle analysis by Razalli, Husniza

    Published 2016
    “…Traditionally, researchers using numerous of ratios obtained from extracted facial features landmark points to measure facial age. …”
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  10. 10
    Product Recommendation System (Targeted Recommendation using Deep Learning in Computer Vision)

    Product Recommendation System (Targeted Recommendation using Deep Learning in Computer Vision) by Sharvinteraan, C. Mogan

    Published 2023
    “…The project workflow involves several key steps. First, the MTCNN algorithm is utilized to detect and extract facial features from images or video streams. …”
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  11. 11
    Facial features for template matching based face recognition

    Facial features for template matching based face recognition by Chai, Tong Yuen, Mohd Rizon, Mohamed Juhari, Prof. Dr., Woo, San San, Tan, Ching Seong, Dr.

    Published 2011
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  12. 12
    Design and development of facial recognitionbased library management system (FRLMS)

    Design and development of facial recognitionbased library management system (FRLMS) by Rao, Vegnish Rao Paramesura, Puwakpitiyage, Chamode Anjana Hewawasam, Abdulkareem Shafiq, Dalia, Islam, Farhana, Handayani, Dini Oktarina Dwi, Yaacob, Hamwira Sakti, Mantoro, Teddy

    Published 2019
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  13. 13
    Facial image retrieval on semantic features using adaptive mean genetic algorithm

    Facial image retrieval on semantic features using adaptive mean genetic algorithm by Shnan, Marwan Ali, Rassem, Taha H., Nor Saradatul Akmar, Zulkifli

    Published 2019
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  14. 14
    Keypoint Descriptors in SIFT and SURF for Face Feature Extractions

    Keypoint Descriptors in SIFT and SURF for Face Feature Extractions by Suk, Ting Pui, Minoi, Jacey Lynn

    Published 2018
    “…The last decade, numerous researches are still working on developing a robust and faster keypoints image descriptors algorithm. …”
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  15. 15
    Analysis of performance between kinect v1 and kinect v2 for various facial part movements

    Analysis of performance between kinect v1 and kinect v2 for various facial part movements by Heng, Sheng Guang, Rosdiyana, Samad, Mahfuzah, Mustafa, Nor Rul Hasma, Abdullah, Pebrianti, Dwi

    Published 2019
    “…A total number of 18 desired facial feature point at similar landmarks will be extracted in the format of 3D coordinates for both Kinect cameras. …”
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  16. 16
    Deep Learning Based Face Attributes Recognition

    Deep Learning Based Face Attributes Recognition by Saidi, Mohamad Hazim

    Published 2018
    “…The addition of convolutional layer is also essential in order to extract related facial features of facial images.…”
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  17. 17
    Digital Assessment of Facial Acne Vulgaris

    Digital Assessment of Facial Acne Vulgaris by Malik, Aamir Saeed, Ramli, Roshaslinie, Hani, Ahmad Fadzil M, Salih, Yasir, Yap, Felix Boon-Bin, Nisar, Humaira

    Published 2014
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  18. 18
    Analysis of performance between kinetic v1 and Kinect v2 for various facial part movements

    Analysis of performance between kinetic v1 and Kinect v2 for various facial part movements by Heng, Sheng Guang, Rosdiyana, Samad, Mahfuzah, Mustafa, Nor Rul Hasma, Abdullah, Pebrianti, Dwi

    Published 2019
    “…A total number of 18 desired facial feature point at similar landmarks will be extracted in the format of 3D coordinates for both Kinect cameras. …”
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  19. 19
    Human Spontaneous Emotion Detection System

    Human Spontaneous Emotion Detection System by Radin Monawir, Radin Puteri Hazimah

    Published 2018
    “…Having smart computerized system which can understand and instantly gives appropriate response to human is the utmost motive in human and computer interaction (HCI) field.It is argued either HCI is considered advance if human could not have natural and comfortable interaction like human to human interaction.Besides,despite of several studies regarding emotion detection system, current system mostly tested in laboratory environment and using mimic emotion.Realizing the current system research lack of real life or genuine emotion input,this research work comes up with the idea of developing a system that able to recognize human emotion through facial expression.Therefore,the aims of this study are threefold which are to enhance the algorithm to detect spontaneous emotion,to develop spontaneous facial expression database and to verify the algorithm performance.This project used Matlab programming language,specifically Viola Jones method for features tracking and extraction,then pattern matching for emotion classification purpose.Mouth feature is used as main features to identify the emotion of the expression.For verification purpose,the mimic and spontaneous database which are obtained from internet,open source database or novel (own) developed databases are used.Basically,the performance of the system is indicated by emotion detection rate and average execution time.At the end of this study,it is found that this system is suitable for recognizing spontaneous facial expression (63.28%) compared to posed facial expression (51.46%).The verification even better for positive emotion with 71.02% detection rate compared to 48.09% for negative emotion detection rate.Finally,overall detection rate of 61.20% is considered good since this system can execute result within 3s and use spontaneous input data which known as highly susceptible to noise.…”
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  20. 20
    Face anti-spoofing using Convolutional Neural Networks / Siti Nurul Izzah Bahrain

    Face anti-spoofing using Convolutional Neural Networks / Siti Nurul Izzah Bahrain by Bahrain, Siti Nurul Izzah

    Published 2024
    “…The study investigates CNN requirements, develops a prototype system, and evaluates its accuracy, achieving an impressive 86% accuracy in detecting fake facial appearances. …”
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