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Accurate Face and Human Detection Using Hybrid Local Transform Features

Chapter
Part of the KAIST Research Series book series (KAISTRS)

Abstract

We propose two novel local transform features: local gradient patterns (LGP) and binary histograms of oriented gradients (BHOG). LGP assigns one if the neighboring gradient of a given pixel is greater than the average of eight neighboring gradients and zero otherwise, which makes the local intensity variations along the edge components robust. BHOG assigns one if the histogram bin has a higher value than the average value of the total histogram bins, and zero otherwise, which makes the feature computation time fast due to no further post-processing and SVM classification. We also propose a hybrid feature that combines several local transform features by AdaBoost feature selection method where the best local transform feature among several local transform features (LBP, LGP, and BHOG), which has the lowest classification error, is sequentially selected until we obtain the required classification performance. This hybridization makes the face and human detection robust to the global illumination change by LBP, the local intensity change by LGP, and the local pose change by BHOG, which improves the detection performance considerably. We apply the proposed local transform features and the hybrid feature to the face detection problem using MIT+CMU and FDDB face database and the human detection problem using INRIA human database. The experimental results show that the proposed LGP and BHOG features attain accurate detection performance and fast computation time, respectively, and the hybrid feature provides a considerable improvement of face detection and human detection.

Keywords

Local binary pattern Local gradient pattern Binary histograms of oriented gradients Feature hybridization Face detection Human detection 

Notes

Acknowledgements

This work is supported by the Center for Integrated Smart Sensors funded by the Ministry of Science, ICT & Future Planning as the Global Frontier Project.

References

  1. 1.
    Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12):2037–2041CrossRefGoogle Scholar
  2. 2.
    Bay H, Ess A, Tuytelaars T, Gool LV (2008) SURF: speeded up robust features. Comput Vis Image Underst 110(3):346–359CrossRefGoogle Scholar
  3. 3.
    Dahmane M, Meunier J (2011) Emotion recognition using dynamic grid-based HoG features. In: Proceedings of IEEE international conference on automatic face and gesture recognition, pp 884–888Google Scholar
  4. 4.
    Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 886–893Google Scholar
  5. 5.
    Deniza O, Buenoa G, Salido J (2011) Face recognition using histograms of oriented gradients. Pattern Recogn Lett 32(12):1598–1603CrossRefGoogle Scholar
  6. 6.
    Dollar P, Belongie S, Perona P (2010) The fastest pedestrian detector in the west. In: Proceedings of the British machine vision conference, pp 1–11Google Scholar
  7. 7.
    Dollar P, Wojek C, Schiele B, Perona P (2009) Pedestrian detection: a benchmark. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 304–311Google Scholar
  8. 8.
    Dollar P, Wojek C, Schiele B, Perona P (2011) Pedestrian detection: an evaluation of the state of the art. IEEE Trans Pattern Anal Mach Intell 34(4):743–761CrossRefGoogle Scholar
  9. 9.
    Enzweiler M, Gavrila DM (2009) Monocular pedestrian detection: survey and experiments. IEEE Trans Pattern Anal Mach Intell 31(12):2179–2195CrossRefGoogle Scholar
  10. 10.
    Felzenszwalb P, Girshick R, McAllester D (2010) Cascade object detection with deformable part models. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 2241–2248Google Scholar
  11. 11.
    Felzenszwalb P, Girshick R, McAllester D, Ramanan D (2010) Object detection with discriminatively trained part based models. IEEE Trans Pattern Anal Mach Intell 32(9):1627–1645CrossRefGoogle Scholar
  12. 12.
    Feng X, Pietikainen M, Hadid A (2005) Facial expression recognition with local binary patterns and linear programming. Pattern Recognit Image Anal 15(2):546–548Google Scholar
  13. 13.
    Froba B, Ernst A (2004) Face detection with the modified census transform. In: Proceedings of IEEE international conference on automatic face and gesture recognition, pp 91–96Google Scholar
  14. 14.
    Grimes DB, Rao RPN (2003) A bilinear model for sparse coding. Neural Inf Process Syst 15:1287–1294Google Scholar
  15. 15.
    Heikkila M, Pietikainen M, Heikkila J (2004) A texture-based method for detecting moving objects. In: Proceedings of British machine vision conference, pp 187–196Google Scholar
  16. 16.
    Heusch G, Rodriguez Y, Marcel S (2006) Local binary patterns as an image preprocessing for face authentication. In: Proceedings of international conference on automatic face and gesture recognition, pp 9–14Google Scholar
  17. 17.
    Huang X, Li SZ, Wang Y (2004) Shape localization based on statistical method using extended local binary pattern. In Proceedings of international conference on image and graphics, pp 184–187Google Scholar
  18. 18.
    Jain V, Miller EL (2010) FDDB: a benchmark for face detection in unconstrained settings. University of Massachusetts, AmherstGoogle Scholar
  19. 19.
    Jin H, Liu Q, Lu H, Tong X (2004) Face detection using improved LBP under Bayesian framework. In: Proceedings of international conference on image and graphics, pp 306–309Google Scholar
  20. 20.
    Ke Y, Sukthankar R (2004) PCA-SIFT: a more distinctive representation for local image descriptors. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 511–517Google Scholar
  21. 21.
    Kellokumpu V, Zhao G, Li S, Pietikainen M (2009) Dynamic texture based gait recognition. In: Proceedings of international conference on biometrics, pp 1000–1009Google Scholar
  22. 22.
    Lowe DG (2004) Distinctive image features from scale invariant keypoints. Int J Comput Vision 60(2):91–110CrossRefGoogle Scholar
  23. 23.
    Mikolajczyk K, Schmid C, Zisserman A (2004) Human detection based on a probabilistic assembly of robust part detectors. In: Proceedings of the European conference on computer vision, pp 69–82Google Scholar
  24. 24.
    Ojala T, Pietikainen M, Harwood D (1996) A comparative study of texture measures with classification based on feature distributions. Pattern Recogn 29(1):51–59CrossRefGoogle Scholar
  25. 25.
    Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution grayscale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987CrossRefGoogle Scholar
  26. 26.
    Papageorgiou C, Poggio T (2000) CA trainable system for object detection. Int J Comput Vision 38(1):15–33CrossRefzbMATHGoogle Scholar
  27. 27.
    Porkili F (2005) Integral histogram: a fast way to extract histograms in cartesian spaces. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 829–836Google Scholar
  28. 28.
    Prisacariu V, Reid I (2009) FastHOG—a real-time GPU implementation of HOG. Department of Engineering Science, Oxford UniversityGoogle Scholar
  29. 29.
    Randen T, Husoy JH (1999) Filtering for texture classification: a comparative study. IEEE Trans Pattern Anal Mach Intell 21(4):291–310CrossRefGoogle Scholar
  30. 30.
    Rowley HA (1999) Neural network-based face detection. Ph.D. thesis, Carnegie Mellon University, PitsburghGoogle Scholar
  31. 31.
    Rowley H, Baluja S, Kanade T (1998) Neural network-based face detection. IEEE Trans Pattern Anal Mach Intell 20(1):23–38CrossRefGoogle Scholar
  32. 32.
    Schneiderman H, Kanade T (2000) A statistical method for 3D object detection applied to faces and cars. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 746–751Google Scholar
  33. 33.
    Shan C, Gong S, McOwan P (2009) Facial expression recognition based on local binary patterns: a comprehensive study. Image Vis Comput 27:803–816CrossRefGoogle Scholar
  34. 34.
    Shet VD, Neumann J, Ramesh V, Davis LS (2007) Bilattice-based logical reasoning for human detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8Google Scholar
  35. 35.
    Subburaman V, Marcel S (2010) Fast bounding box estimation based face detection. In: Proceedings of ECCV workshop on face detection: where we are and what next?Google Scholar
  36. 36.
    Sun N, Zheng W, Sun C, Zou C, Zhao L (2006) Gender classification based on boosting local binary pattern. In: Proceedings of international symposium on neural networks, pp 194–201Google Scholar
  37. 37.
    Swain M, Ballard D (1991) Color indexing. Int J Comput Vision 7(1):11–32CrossRefGoogle Scholar
  38. 38.
    Takala V, Ahonen T, Pietikainen M (2005) Block-based methods for image retrieval using local binary patterns. In: Proceedings of Scandinavian conference on image analysis, pp 882–891Google Scholar
  39. 39.
    Viola P, Jones M (2004) Robust real-time face detection. Int J Comput Vision 57(2):137–154CrossRefGoogle Scholar
  40. 40.
    Viola P, Jones M, Snow D (2005) Detecting pedestrians using patterns of motion and appearance. Int J Comput Vision 63(2):153–161CrossRefGoogle Scholar
  41. 41.
    Yan S, Shan S, Chen X, Gao W (2008) Locally assembled binary (LAB) feature with feature-centric cascade for fast and accurate face detection. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 1–7Google Scholar
  42. 42.
    Zabih R, Woodfill J (1994) Non-parametric local transforms for computing visual correspondence. In: Proceedings of European conference on computer vision, pp 151–158Google Scholar
  43. 43.
    Zhang L, Chu R, Xiang S, Liao S, Li S (2007) Face detection based on multi-block LBP representation. In: Proceedings of international conference on biometrics, pp 11–18Google Scholar
  44. 44.
    Zhang W, Shan S, Gao W, Chen X, Zhang H (2005) Local gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition. In Proceedings of IEEE international conference on computer vision, pp 786–791Google Scholar
  45. 45.
    Zhang L, Wu B, Nevatia R (2007) Detection and tracking of multiple humans with extensive pose articulation. In: Proceedings of IEEE international conference on computer vision, pp 1–8Google Scholar
  46. 46.
    Zhu Q, Avidan S, Yeh M, Cheng K (2006) Fast human detection using a cascade of histograms of oriented gradients. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 1491–1498Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringPohang University of Science and TechnologyGyeongbukKorea
  2. 2.StradVisionGyeongbukKorea

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