| Penulis/Author |
Prof. Ir. Hanung Adi Nugroho, S.T., M.Eng., Ph.D., IPM., SMIEEE. (1); Teguh Bharata Adji, S.T., M.T., M.Eng., Ph.D (2) |
| Abstrak/Abstract |
In the era of the 2000s, the development of
internet technology started to grow rapidly resulting in immense
number of information available on the internet. However, the
trend towards free internet access causes some adverse impacts on
the society due to the negative contents (pornography) which are
bounded on some finding information. The aim of this research is
to develop a filtering system of negative content based on nipple
detection, as one of the body vital parts.
The proposed scheme uses Haar-Cascade classifier which is
trained by 1000 positive image data (nipple images) and 8500
negative image data (no nipple-images). Beforehand, face
detection is conducted to decrease misclassifying (false positive)
detection around the face area. Feature extraction process uses
84 attributes of GLCM and 12 attributes of colour statistics on
the nipple of the object candidates. Furthermore, MLP is
conducted to classify these candidates with 10 neurons and a
hidden layer for the MLP architecture.
As a result, by using 160 nipple data, 12 features of colour
statistics achieve the best performance with the accuracy of 90%
and sensitivity of 96.3% compared to 84 GLCM features and 96
all features. In comparison to the conventional method which
used 27 images data, the accuracy and specificity values are
increased to 87% and 94%, respectively. However, from the
consumer side, they prefer to choose the best specificity value by
using 84 attributes of GLCM with specificity value of 87.34%.
The consumers may be disturbed by the situation in which the
non-porn images are classified as prohibited images resulting in
the blocking of the system. |
