Version 1
: Received: 10 September 2020 / Approved: 11 September 2020 / Online: 11 September 2020 (12:10:28 CEST)
How to cite:
Bandyopadhyay, S.; Bose, P. Human Face Detection: Manual vs. Kohonen Self Organizing Map. Preprints2020, 2020090257. https://doi.org/10.20944/preprints202009.0257.v1
Bandyopadhyay, S.; Bose, P. Human Face Detection: Manual vs. Kohonen Self Organizing Map. Preprints 2020, 2020090257. https://doi.org/10.20944/preprints202009.0257.v1
Bandyopadhyay, S.; Bose, P. Human Face Detection: Manual vs. Kohonen Self Organizing Map. Preprints2020, 2020090257. https://doi.org/10.20944/preprints202009.0257.v1
APA Style
Bandyopadhyay, S., & Bose, P. (2020). Human Face Detection: Manual vs. Kohonen Self Organizing Map. Preprints. https://doi.org/10.20944/preprints202009.0257.v1
Chicago/Turabian Style
Bandyopadhyay, S. and Payel Bose. 2020 "Human Face Detection: Manual vs. Kohonen Self Organizing Map" Preprints. https://doi.org/10.20944/preprints202009.0257.v1
Abstract
In today's world it is very much important to maintain the security of information and its risks. The biometric-based techniques are very much useful in these problems. Among the several kinds of biometric-based technique, face detection is much complex and much more important. Due to the age and several other problems, a human face structure changes over time, again a human has lots of expressions. Sometimes due to the lighting condition or the variation of the angle of an input device, the pattern of a human face structure also changed. As a result, the face cannot be detected properly. In this paper, a method is proposed that can detect the human faces both automatically and manually very efficiently. In manual mode, a user can select the input faces referred by the system according to their choice. In automated mode, the system detected all possible face areas using the Kohonen Self-Organizing Feature Map technique. This method reduced the complex color image into a vector quantized image with desired colors. Then a color segmentation technique is used to detect the possible face skin areas from the vector quantized image. Then the Histogram Oriented Gradient technique used to detect the feature from the faces and K-Nearest Neighbour Classifier is used to compare both face images detected by the two modes. The automated method prosed better accuracy than the manual method.
Keywords
Face Detection; Kohonen Self-Organizing Feature Map(K-SOM); Skin Color Segmentation; K-Nearest Neighbour (KNN) Classifier
Subject
Computer Science and Mathematics, Applied Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
