Human Face Detection: Manual vs. Kohonen Self Organizing Map

  • Payal Bose Research Scholar at Lincoln University College, Lembah Sireh, 15050 Kota Bharu, Kelantan, Malaysia
  • Prof. Samir K Bandyopadhyay Distinguish Professor at Lincoln University College, Lembah Sireh, 15050 Kota Bharu, Kelantan, Malaysia
Keywords: Face Detection, Kohonen Self-Organizing Feature Map (K-SOM), Skin Color Segmentation, K-Nearest Neighbor (KNN) Classifier


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 Neighbor Classifier is used to compare both face images detected by the two modes. The automated method prosed better accuracy than the manual method.


. H. Mo, B. Xu, W. Ouyang, and J. Wang, “Color segmentation of multi-colored fabrics using self-organizing-map based clustering algorithm,” Text. Res. J., vol. 87, no. 3, pp. 369–380, 2017, doi: 10.1177/0040517516631307.

. S. S. Sofi and R. A. Khan, “Face recognition using neural network technique som (Self organizing maps),” Int. J. Sci. Technol. Res., vol. 8, no. 12, pp. 3423–3427, 2019.

. F. Liu, S. Yang, Y. Ding, and F. Xu, “Single sample face recognition via BoF using multistage KNN collaborative coding,” Multimed. Tools Appl., vol. 78, no. 10, pp. 13297–13311, 2019, doi: 10.1007/s11042-018-7002-5.

. S. S. Kumar, P. K. Sahoo, and K. Eswaran, “Class Room Attendance System Using KNN,” no. April, 2019.

. B. S. Sathish, “Segmentation in RGB and HSV Color Space,” pp. 2–7, 2015.

. S. Ghorpade, J. Ghorpade, S. Mantri, and D. Ghorpade, “Neural Networks For Face Recognition Using SOM,” vol. 4333, pp. 65–67, 2010.

. N. Qu, J. Chen, J. Zuo, and J. Liu, “PSO-SOM Neural Network Algorithm for Series Arc Fault Detection,” Adv. Math. Phys., vol. 2020, 2020, doi: 10.1155/2020/6721909.

. S. Kim and J. Lee, “Facial Shape Recognition Using Self Organized Feature Map ( SOFM ),” vol. 8, no. 4, pp. 104–112, 2019.

. O. E. Dragomir, F. Dragomir, and M. Radulescu, “Matlab application of Kohonen Self-Organizing Map to classify consumers’ load profiles,” Procedia Comput. Sci., vol. 31, pp. 474–479, 2014, doi: 10.1016/j.procs.2014.05.292.

. R. Mohanty and M. V Raghunadh, “Skin Color Segmentation based Face Detection using Multi-Color Space,” vol. 5, no. 5, pp. 470–475, 2016, doi: 10.1021/la900547b.

. C. Q. Lai and S. S. Teoh, “An efficient method of HOG feature extraction using selective histogram bin and PCA Feature reduction,” Adv. Electr. Comput. Eng., vol. 16, no. 4, pp. 101–108, 2016, doi: 10.4316/AECE.2016.04016.

. W. Zhou, S. Gao, L. Zhang, and X. Lou, “Histogram of Oriented Gradients Feature Extraction from Raw Bayer Pattern Images,” IEEE Trans. Circuits Syst. II Express Briefs, vol. 67, no. 5, pp. 946–950, 2020, doi: 10.1109/TCSII.2020.2980557.

. L. Wang, “Research and Implementation of Machine Learning Classifier Based on KNN,” IOP Conf. Ser. Mater. Sci. Eng., vol. 677, no. 5, 2019, doi: 10.1088/1757-899X/677/5/052038.

. N. Ali, D. Neagu, and P. Trundle, “Evaluation of k-nearest neighbour classifier performance for heterogeneous data sets,” SN Appl. Sci., vol. 1, no. 12, 2019, doi: 10.1007/s42452-019-1356-9.

. L. Feng, H. Li, Y. Gao, and Y. Zhang, “A Color Image Segmentation Method Based on Region Salient Color and Fuzzy C-Means Algorithm,” Circuits, Syst. Signal Process., vol. 39, no. 2, pp. 586–610, 2020, doi: 10.1007/s00034-019-01126-w.

. X. Zheng, Q. Lei, R. Yao, Y. Gong, and Q. Yin, “Image segmentation based on adaptive K-means algorithm,” Eurasip J. Image Video Process., vol. 2018, no. 1, 2018, doi: 10.1186/s13640-018-0309-3.

How to Cite
Bose, P., & Bandyopadhyay, P. S. K. (2020). Human Face Detection: Manual vs. Kohonen Self Organizing Map. International Journal of Computer (IJC), 39(1), 79-87. Retrieved from