SavedAdd your journal

Journals / Selçuk Üniversitesi Mühendislik Bilim ve Teknoloji Dergisi / 2017 / Cilt: 5 - Sayı: 4

Yüz İfade Tanınması İçin Bir Monojenik Yerel Gabor İkili Desen

A MONOGENIC LOCAL GABOR BINARY PATTERN FOR FACIAL EXPRESSION RECOGNITION

Abubakar M ASHIR
Pages
414–422
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Experimental results from JAFFE and TFEID facial expression databases show that the new technique has improved performance compared to its counterparts

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The paper implements a monogenic-Local Binary Pattern (mono-LBP) algorithm on Local Gabor Pattern (LGP).
📊
Key Findings & Data:The extracted LGP features are further enhanced by decomposing it into three monogenic LBP channels before being recombined to generate the final feature vector.
💡
Conclusion & Impact:Experimental results from JAFFE and TFEID facial expression databases show that the new technique has improved performance compared to its counterparts
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

The paper implements a monogenic-Local Binary Pattern (mono-LBP) algorithm on Local Gabor Pattern (LGP). The proposed approach initially features from the samples using LGP at different scales and orientation. The extracted LGP features are further enhanced by decomposing it into three monogenic LBP channels before being recombined to generate the final feature vector. Different Normalization schemes are applied to the final feature vector. Two best performing normalization algorithms with mono-LBP are fused at score level to obtain an improved performance using K-Nearest Neighbor classifier with L1-norm as a distance metrics. Moreover, performance comparison is done with other variants of LGP algorithm and also the effects of various normalization techniques are investigated. Experimental results from JAFFE and TFEID facial expression databases show that the new technique has improved performance compared to its counterparts

Özet

Bu makale, yerel Gabor Desen (LGP) üzerinde monojenik-Yerel İkili Desen (mono-LBP) algoritmasını uygular. Önerilen algoritma Gabor çekirdeğinin farklı ölçeklerinde ve farklı normalizasyon şemaları ile uygulanır. Mono-LBP ile en iyi performans gösteren normalleştirme algoritmalarından elde edilen sonuçlar, geliştirilmiş bir performans elde etmek için skor düzeyinde birleştirilmiştir. Üstelik, performans karşılaştırması diğer LGP algoritmasının türevleri ile yapılmıştır ve ayrıca çeşitli normalleştirme tekniklerinin etkileri araştırılmaktadır. JAFFE yüz ifadesi veritabanında yapılan deneysel sonuçlarine göre, önerilen yaklaşım bir sınıflandırıcı olarak mesafe metrikini kullanarak mevcut algoritmalara kıyasla en iyi ortalama performansa sahip olduğunu göstermektedir

🔖

Cite This Article

ASHIR, A. M. (2017). Yüz İfade Tanınması İçin Bir Monojenik Yerel Gabor İkili Desen. Selçuk Üniversitesi Mühendislik Bilim ve Teknoloji Dergisi, Cilt: 5 - Sayı: 4, 414–422. https://ajindex.com/articles/317871

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (11)

  1. Ahonen T., Hadid A., Pietikainen M., 2006, “Face Description with Local Binary Patterns: Application to Face Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28(12), pp. 2037-2041.
  2. Chao, W. L., Ding, J. J., Liu, J. Z., 2015, “Facial Expression Recognition based on Improved Local Binary Pattern and Class-regularized Locality Preserving Projection”, Signal Processing, Vol. 117, pp. 1- 10. Chen, L. F., Yen, Y. S., 2007, “Taiwanese Facial Expression Image Database”, Brain Mapping Laboratory, Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan. Cootes, T. F., Taylor, C. J., Cooper, D. H., Graham, J., 1995, “Active Shape Models, Their Training and Application,” Computer Vision and Image Understanding, Vol. 61(1), pp. 38-59.
  3. Doughman, J., 2001, “High Confidence Recognition of Persons by Iris Patterns”, IEEE International Carnahan Conference on Security Technology, London, England, pp. 254-263, 16-19 October 2001.
  4. Eleyan, A., Demirel, H., Özkaramanli, H., 2008, “Complex Wavelet Transform-Based Face Recognition”, EURASIP Journal on Advances in Signal Processing, Vol. 2008, pp. 1-13.
  5. Liu, C., Wechsler, H., 2003, “Independent Component Analysis of Gabor Features for Face Recognition”, IEEE Transactions on Neural Networks, Vol. 14(4), pp. 919-928.
  6. Lyons, M., Akamatsu, S., Kamachi, M. and Gyoba, J., 1998, “Coding Facial Expressions with Gabor Wavelets”, 3rd IEEE International Conference on Automatic Face and Gesture Recognition (AFGR), Nara, Japan, pp. 200-205, 14-16 April 1998.
  7. Nandakumar, K., Jain, A., Ross, A., 2005, “Score Normalization in Multimodal Biometric Systems”, The Journal of Pattern Recognition Society, Elservier, Vol. 38, pp. 2270-2285.
  8. Ojala, T., Pietikainen, M., Maenpaa, T., 2010, “Multi-resolution Gray Scale and Rotation Invariant Texture Analysis with Local Binary Patterns”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24(7), pp. 971-987.
  9. Ribaric S., Fratric I., 2006, “Experimental Evaluation of Matching-Score Normalization Techniques on Different Multimodal Biometric Systems”, IEEE Mediterranean Electrotechnical Conference, MELECON, Malaga, Spain, pp. 498-501, 16-19 May 2006.
  10. Sigdel, M., Dinc, S., Sigdel, M. S., Pusey, M. L., Aygun, R. S., 2014, “Evaluation of Normalization and PCA on the Performance of Classifiers for Protein Crystallization Images” IEEE Conference on SOUTHEASTCON, Lexington, KY, USA, pp. 1-6, 13-16 March 2014.
  11. Tran C. K., Lee T. F., Chang L., Chao P. J