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Dergiler / International Journal of Environment and Geoinformatics / 2017 / Cilt: 4 - Sayı: 2

A novel approach to automatic detection of interest points in multiple facial images

Bülent BAYRAM
G. Çiğdem ÇAVDAROĞLU
Dursun Zafer ŞEKER
Sıtkı KÜLÜR
Sayfa
116–127
Yayın Tarihi
2017-05-15
DOI
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Yapay Zeka Hızlı Bakış & Çıkarımlar

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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The average accuracy of facial interest point detection was 0.68 mm after testing all images.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:The human face includes different colors and forms due to its complexity.
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Temel Bulgular & Veri:The study used processed facial images from an authorized image database with a resolution of 1600 x 1200, taken in standardized illumination conditions by using an InSpeck Mega Capturor II optical 3D structured light digitizer and 1000-W halogen lamp.
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Sonuç & Katkı:The average accuracy of facial interest point detection was 0.68 mm after testing all images.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Close-range photogrammetry · face recognition and facial interest points · image matching and processing

Abstract

The human face includes different colors and forms due to its complexity. Therefore, facial image processing comprises even more problems than image processing of other objects. Interest point detection is one of the important problems in computer vision, which is the key aspect of solving problems such as facial expression analysis, age analysis, sex defining, facial recognition, and three-dimensional face modelling in augmented reality. To accomplish these tasks, facial interest points need automatic definition. A hybrid algorithm was developed to detect automatically interest regions and points in multiple images in the resented study. The study used processed facial images from an authorized image database with a resolution of 1600 x 1200, taken in standardized illumination conditions by using an InSpeck Mega Capturor II optical 3D structured light digitizer and 1000-W halogen lamp. The presented study integrated skin color analysis with the Haar classification method, processing 11 male and 25 female facial images with the developed algorithm. The average accuracy of facial interest point detection was 0.68 mm after testing all images.

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Bu Makaleye Atıf Yap

BAYRAM, B., ÇAVDAROĞLU, G. Ç., ŞEKER, D. Z., KÜLÜR, S. (2017). A novel approach to automatic detection of interest points in multiple facial images. International Journal of Environment and Geoinformatics, Cilt: 4 - Sayı: 2, 116–127. https://ajindex.com/articles/672626

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Kaynakça (12)

  1. Ar, I. (2008). Recognition of Human Behaviours In Video Streams. Seminar report, Gebze Institute of Technology, Dept. of Informatics, Izmit-Turkey. Bansal, A. (2012). Face recognition using PCA and LDA Algorithm. In: Advanced Computing and Communication Technologies (ACCT), Second International Conference. Rohtak- India, 251-254.
  2. Bhowmik, M.K., Shil, S. and Saha, P. (2013). Feature Points Extraction of Thermal Face using Harris Interest Point Detection. In: International Conference on Computational Intelligence: Modeling Techniques and Applications (CIMTA), University of Kalyani. West Bengal, India, September 27-28 2013, Procedia Technology 2013; 10: 724 – 730. Bhumika, G. and Zankhana, H. (2011). Face Feature Extraction Techniques: A Survey. In: National Conference on Recent Trends in Engineering & Technology. Anand, Gujarat, India, 13-14 May 2011.
  3. Brunelli, R. and Poggio, T. (1993). Face Recognition: Features Versus Templates, IEEE Transactions on PAMI, 15(10), 1042-1052.
  4. Çavdaroğlu, G.Ç. (2013). Face Recognition Analysis by Developing Feature Operators In Virtual Reality, MSc Thesis, Yildiz Technical University, Institute of Natural and Applied Sciences, Istanbul-Turkey. Chai, D. and Ngan, K. (1999). Face Segmentation Using Skin Color Map in Videophone Application. Transactions on Circuits and Systems for Video Technology, 9(4): 551-564.
  5. Davies, E.R. (2012).
  6. Corner and Interest Point Detection, Computer and Machine Vision (Fourt Edition), Chapter 6: 149-184.
  7. Delakis, M. and Garcia, C. (2002). Robust Face Detection Based on Convolutional Neural Networks, 2nd Hellenic Conf. on Al., SETN-2002, 11-12 April 2002,
  8. Thessaloniki, Gereece, Proceesing: 367-378.
  9. Demirel, H. and Anbarjafari, G. (2008). A New Face Recognition System Based On Color Histogram Matching. In: Signal Processing, Communication and Applications Conference, SIU, 22-28
  10. April, Aydin, Turkey: 1–4.
  11. Eser, S. (2006). Face Recognition And Tracking By Neural Networks, MS Thesis, Yildiz Technical University, Institute of Natural and Applied Sciences, Istanbul-Turkey. Feris, R.S., Gemmell, J., Toyama, K. and Krüger, V. (2002). Hierarchical Wavelet Networks for Facial Feature Localization. In: Proceedings of the 5th International Conference on Automatic Face and Gesture Recognition. Washington D.C. USA, 21 May, 2002: 118–123.
  12. Fröba, B. and Küblbeck, C. (2001). Real-Time Face Detection Using Edge-Orientation Matching. In: Third International Conference, AVBPA, Lecture Notes in Computer Science, Halmsta