Geometrical Structure Based the Greatest Gradation for the Hatching Facial Sketch Features Detection


  • Arif Muntasa Informatics Engineering, Faculty of Engineering Trunojoyo University
  • Mochammad Kautsar Sophan Informatics Engineering, Faculty of Engineering, Trunojoyo University


Geometrical stricture, the greatest gradation, facial sketch, feature detection


Deformable object modeling is an interest topic on biometrics field, such as the facial features detection. One of method used to detect the deformable objects is Active Shape Model method. However, Active Shape Model has some weaknesses, when detection process is performed. The first weakness, ASM needs many training sets to obtain the shape variation. If the movement direction is not covered on the training sets, then ASM cannot overcome the shape movement to the corresponding features. The second weakness, if the shape initialization is not closed to the corresponding features, then the features searching process will need long time and even fail to detect the corresponding features. In this research, we proposed new approach to detect the
facial sketch features. The shape will move to the corresponding features based on the greatest gradation, though the movement direction is not covered on the training set. The shape initialization will be located based on three parameters, which are the landmark average, variation average and the difference of the deviation maximum and minimum. We have tested 200 the hatching facial images, they consist of 100 the hatching facial images tilted to the left and the rest is tilted to the right. Experimental shows that detection percentage is 88.47% and 88.86% for the hatching facial sketch tilted to the right and to the left .


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How to Cite

Muntasa, A., & Sophan, M. K. (2020). Geometrical Structure Based the Greatest Gradation for the Hatching Facial Sketch Features Detection. International Journal on Information Technology and Computer Science, 5(1). Retrieved from



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