Neonate facial gender classification using PCA and fuzzy clustering
Publish place: 17th Iranian Conference on Biomedical Engineering
Publish Year: 1389
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
ICBME17_100
تاریخ نمایه سازی: 9 تیر 1392
Abstract:
This paper considers the problem of neonate gender classification using frontal facial image. Determining sex ofneonates using facial image is a challenging issue even for human observers. We propose a new gender classification method for neonate facial image by employing Principal Component Analysis (PCA) and Fuzzy C-means Algorithm (FCM). In this approach, PCA is used to extract suitable features with reduced dimensional space. These features are then used to assign the image to an appropriate class, hence recognizing it as belongingto a boy or a girl. This technique can be used to assist physicians in recognizing intersex neonates. Compared to the clinical approaches, such as hormonal, genetic and radiological methods, the proposed approach is fast and inexpensive. In an experiment performed on 48 neonate facial images, the naive human observers could classify the gender with 58.33% accuracy while the proposed method outperformed with 91.66% accuracy.
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Authors
Hamid Hasassnpour
School of Computer Engineering Shahrood University of Technology Shahrood, Iran
Hossein Dehghani
School of Computer Engineering Shahrood University of Technology Shahrood, Iran
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