Search Results - (( facial detection method algorithm ) OR ( java application testing algorithm ))

Search alternatives:

Refine Results
  1. 1
    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
    Get full text
    Article
    Save to List
    Saved in:
  2. 2
    Integrated face and facial components detection

    Integrated face and facial components detection by Ho, Lip Chin, Hanafi, Marsyita, Salka, Tanko Danial

    Published 2015
    “…This paper presents an algorithm that detects faces and facial features (eyes, nose and mouth) on images captured by CCTV system under various imaging conditions, such as variation in poses, scale, illumination and occlusion. …”
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  3. 3
    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
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  4. 4
    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
    Get full text
    Working Paper
    Save to List
    Saved in:
  5. 5
    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
    “…The experiment conducted on challenging 3D face databases yields good results with 94.77% detection rate for the nose tip region detection algorithm. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  6. 6
    Deep learning methods for facial expression recognition

    Deep learning methods for facial expression recognition by Mohammad Masum Refat, Chowdhury, Zainul Azlan, Norsinnira

    Published 2019
    “…In this paper, we analyze various deep learning methods and their results. We have chosen Deep convolutional neural network as the best algorithms for facial expression detection and classification. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Proceeding Paper
    Save to List
    Saved in:
  7. 7
    Facial Drowsiness Signs Detection Algorithm Using Image Processing Techniques For Various Lighting Condition

    Facial Drowsiness Signs Detection Algorithm Using Image Processing Techniques For Various Lighting Condition by Yuri, Nur Fatin Izzati

    Published 2017
    “…In this project, the algorithm is developed based on four main algorithms which are the detection algorithm, the tracking algorithm, the preprocessing algorithm and the drowsiness signs analysis algorithm. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  8. 8
    Facial expression type recognition using K-Nearest Neighbor algorithm / Norhafizah Saffian

    Facial expression type recognition using K-Nearest Neighbor algorithm / Norhafizah Saffian by Saffian, Norhafizah

    Published 2017
    “…The facial expression consists of three steps that are face detection, facial feature extraction, and classification of feature extraction. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  9. 9
    Mental health detection based on facial recognition using CNN algorithm / Muhammad Azim Azfar Rozihan

    Mental health detection based on facial recognition using CNN algorithm / Muhammad Azim Azfar Rozihan by Rozihan, Muhammad Azim Azfar

    Published 2025
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  10. 10
    GPSO versus GA in facial emotion detection

    GPSO versus GA in facial emotion detection by Bashir, Mohammed Ghandi, Ramachandran, Nagarajan, Prof. Dr., Sazali, Yaacob, Prof. Dr., Hazry, Desa, Assoc. Prof. Dr.

    Published 2012
    Get full text
    Working Paper
    Save to List
    Saved in:
  11. 11
    Age detection from face using Convolutional Neural Network (CNN) / Hanin Hanisah Usok @ Yusoff

    Age detection from face using Convolutional Neural Network (CNN) / Hanin Hanisah Usok @ Yusoff by Usok @ Yusoff, Hanin Hanisah

    Published 2024
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  12. 12
    Automated face localization and facial features detection using geometric information

    Automated face localization and facial features detection using geometric information by Chai, Tong Yuen, Mohd Rizon, Mohamed Juhari, Prof. Dr., Karthigayan, M., Sugisaka, M., Hazry, Desa, Dr., Ibrahim, Z.

    Published 2011
    Get full text
    Working Paper
    Save to List
    Saved in:
  13. 13
    Property premises intruders detection using face recognition method / Joveni Henry

    Property premises intruders detection using face recognition method / Joveni Henry by Joveni Henry

    Published 2017
    “…This project will focus mainly on the Viola-Jones algorithm for face and facial parts detection, facial geometry distance measure for feature extraction and Similarity Measure algorithm using the Euclidian Distance to perform face recognition. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  14. 14
    Age-based facial recognition using convoluted neural network deep learning algorithm

    Age-based facial recognition using convoluted neural network deep learning algorithm by Julius Yong Wu Jien, Aslina Baharum, Shaliza Hayati A. Wahab, Nordin Saad, Muhammad Omar, Noorsidi Aizuddin Mat Noor

    Published 2020
    Get full text
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  15. 15
    Evaluation of facial detection using adaboost and morphology / Maisarah Aripin

    Evaluation of facial detection using adaboost and morphology / Maisarah Aripin by Aripin, Maisarah

    Published 2007
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  16. 16
    Computing non-contactable drowsiness monitoring system with mobile machine vision

    Computing non-contactable drowsiness monitoring system with mobile machine vision by Alixson Polumpung, Lorita Angeline, Helen Sin Ee Chuo, Tan, Min Keng, Lim, Kit Guan, Teo, Kenneth Tze Kin

    Published 2022
    “…The second method, Viola-Jones which use Haar feature to detect facial feature such as face and eye also developed and tested. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  17. 17
    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
    “…Most of those points are obtained from publicly facial aging database. Although the estimation result promising, the method still have limitation because it’s work with manual calibration to detect, to extract all the landmark point to estimate human facial age. …”
    Get full text
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  18. 18
    Face detection in image sequence / Noor Diana Abdullah

    Face detection in image sequence / Noor Diana Abdullah by Abdullah, Noor Diana

    Published 2007
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  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.…”
    Get full text
    Get full text
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  20. 20
    Multilocal feature selection using genetic algorithm for face identification

    Multilocal feature selection using genetic algorithm for face identification by Mohamad, Dzulkifli

    Published 2008
    “…The approach taken in this work consists of five stages, namely face detection, facial feature (eyes, nose and mouth) extraction, moment generation, facial feature classification and face identification. …”
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:

Search Tools: