Framework for Face Detection
TL;DR: K-means clustering has been used for distinguishing skin from non skin region, and some morphological operations have been performed to clean the image and region property measures have been used to locate face in the image.
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Abstract: This paper shows an approach to the detection and identification of human faces in a color image. Detection of human faces is becoming a very important task in various applications, such as video surveillance and security control system, intelligent human computer interface, face recognition, content-based image retrieval, multimedia applications on the web like video conferencing and face database management. In this paper, k-means clustering has been used for distinguishing skin from non skin region. Then, some morphological operations have been performed to clean the image. Finally, region property measures have been used to locate face in the image.
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Citations
•Journal Article
A comparison of face detection algorithms
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TL;DR: It is found that representations based on local receptive fields such as those in Rowley, Baluja, and Kanade consistently provide better performance than full connectivity approaches and ensemble techniques, especially those using active sampling such as AdaBoost and Bootstrap, consistently improve performance.
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A Fusion based Approach of Face Detection using Viola Jones and Skin Color Modeling Technique
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TL;DR: This paper focuses on analyzing the existing faces detection techniques and combining some compatible techniques to form a model with improved efficiency and better detection rate.
References
Detecting faces in images: a survey
TL;DR: In this article, the authors categorize and evaluate face detection algorithms and discuss relevant issues such as data collection, evaluation metrics and benchmarking, and conclude with several promising directions for future research.
Face Detection
Erik Hjelmås,Boon Low +1 more
TL;DR: A comprehensive and critical survey of face detection algorithms, ranging from simple edge-based algorithms to composite high-level approaches utilizing advanced pattern recognition methods, is presented.
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A modified version of the K-means algorithm with a distance based on cluster symmetry
Mu-Chun Su,Chien-Hsing Chou +1 more
TL;DR: In this article, a modified version of the K-means algorithm is proposed to cluster data, which adopts a novel nonmetric distance measure based on the idea of "point symmetry", which can be applied in data clustering and human face detection.
A Robust Skin Color Based Face Detection Algorithm
Sanjay Singh,D. S. Chauhan,Mayank Vatsa,Richa Singh +3 more
- 01 Dec 2003
TL;DR: Experimental results show that the proposed algorithm is good enough to localize a human face in an image with an accuracy of 95.18%.
Face detection using skin tone segmentation
Sayantan Thakur,Sayantanu Paul,Ankur Mondal,Swagatam Das,Ajith Abraham +4 more
- 01 Dec 2011
TL;DR: An improved segmentation algorithm for face detection in color images with multiple faces and skin tone regions is proposed, which ingeniously uses a novel skin color model, RGB-HS-CbCr for the detection of human faces.