Abstract
In this work, we propose a new formulation of the objects modeling combining geometry and appearance; it is useful for detection and recognition. The object local appearance location is referenced with respect to an invariant which is a geometric landmark. The appearance (shape and texture) is a combination of Harris–Laplace descriptor and local binary pattern (LBP), all being described by the invariant local appearance model (ILAM). We use an improved variant of LBP traits at regions located by Harris–Laplace detector to encode local appearance. We applied the model to describe and learn object appearances (e.g., faces) and to recognize them. Given the extracted visual traits from a test image, ILAM model is carried out to predict the most similar features to the facial appearance: first, by estimating the highest facial probability and then in terms of LBP histogram-based measure, by computing the texture similarity. Finally, by a geometric calculation the invariant allows to locate an appearance in the image. We evaluate the model by testing it on different face images databases. The experiments show that the model results in high accuracy of detection and provides an acceptable tolerance to the appearance variability.
Similar content being viewed by others
References
Burns J, Weiss R, Riseman E (1993) View variation of point set and line-segment features. PAMI 15(1):51–68
Dorko G, Schmid C (2003) Selection of scale-invariant parts for object class recognition. In: ICCV, pp 634–640
Agarwal S, Awan A, Roth D (2004) Learning to detect objects in images via a sparse, part-based representation. PAMI 26(11):1475–1490
Fei-Fei L, Fergus R, Perona P (2003) A Bayesian approach to unsupervised one-shot learning of object categories. ICCV. Nice, France, pp 1134–1141
Hadid A, Pietikäinen M, Ahonen T (2004) A discriminative feature space for detecting and recognizing faces. In: Computer vision and pattern recognition (CVPR), proceedings of the IEEE computer society conference, vol 2, pp 797–804
Ojala T, Pietikäinen M, Mäenpää T (2002) Multi-resolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell (PAMI) 24:971–987
Taffar M, Miguet S (2017) Face class modeling based on local appearance for recognition. In: 6th international conference on pattern recognition applications and methods (ICPRAM’17), Porto, Portugal
Visual Object Classes database (2012) Pattern analysis, statistical modelling and computational learning (PASCAL) visual object classes challenge. http://host.robots.ox.ac.uk/pascal/VOC/voc2012/
Color FERET Face Database (2009). www.itl.nist.gov/iad/humanid/colorferet
CMU Face Group and Face Detection Project (2009) Frontal and profile face images databases. http://vasc.ri.cmu.edu/idb/html/face/
CMU-PIE Database, CMU Pose, illumination, and expression (PIE) database. http://www.ri.cmu.edu/projects/project_418.html
AT&T Database (1994) AT&T: the database of faces, Cambridge University, Computer Laboratory, Digital Technology Group. http://www.cl.cam.ac.uk/Research/DTG/attarchive:pub/data/att_faces.zip
Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2015) Object detectors emerge in deep scene CNNs. In: ICLR
Zhu Z, Luo P, Wang X, Tang X (2014) Multi-view perceptron: a deep model for learning face identity and view representations. In: NIPS
LeCun Y, Bengio Y, Hinton G (2015) Deep learning, nature, vol 521. Macmillan Publishers, London, pp 436–444
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. IJCV 60(2):91–110
Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Proceedings of CVPR, vol 1, pp 886–893
Shen L, Bai L (2006) A review on Gabor wavelets for face recognition. Pattern Anal Appl 9(2–3):273–292
Kumar N, Berg AC, Belhumeur PN, Nayar SK (2009) Attribute and simile classifiers for face verification. In: 12th IEEE conference on ICCV, pp 365–372
Huang GB, Lee H, Learned-Miller E (2012) Learning hierarchical representations for face verification with convolutional deep belief networks. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 2518–2525
Sun Y, Wang X, Tang X (2013) Hybrid deep learning for face verification. In: ICCV
Chopra S, Hadsell R, LeCun Y (2005) Learning a similarity metric discriminatively, with application to face verification. In: IEEE conference on CVPR, vol 1, pp 539–546
Toews M, Arbel T (2006) Detection over viewpoint via the object class invariant. Proc Int Conf Pattern Recogn 1:765–768
Taffar M, Benmohammed M (2011) Generic face invariant model for face detection. In: Proceedings of the IP&C conference. Springer, New York, pp 39–45
Fergus R, Perona P, Zisserman A (2003) Object class recognition by unsupervised scale-invariant learning. In: CVPR, Madison, Wisconsin, pp 264–271
Lindeberg T (1998) Feature detection with automatic scale selection. Int J Comput Vis 30(2):79–116
Mikolajczyk K, Schmid C (2004) Scale and affine invariant interest point detectors. IJCV 60(1):63–86
Kadir T, Brady M (2001) Saliency, scale and image description. IJCV 45(2):83–105
Herbert B, Tinnr T, Gool LV (2006) SURF: speeded up robust features. In: ECCV, Springer LNCS, vol. 3951(1), pp 404–417
Heisele B, Poggio T, Pontil M (2000) Face detection in still gray images. Technical report 1687, Center for Biological and Computational Learning, MIT
Yang M-H, Kriegman DJ, Ahuja N (2002) Detecting faces in images: a survey. IEEE Trans Pattern Anal Mach Intell (PAMI) 24:34–58
Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: Proceedings of the computer vision and pattern recognition (CVPR), pp 511–518
Taffar M, Miguet S, Benmohammed M (2012) Viewpoint invariant face detection, networked digital technologies, communications in computer and information science. Springer, New York, pp 90–402
Turk M, Pentland A (1991) Eigenfaces for recognition. J Cognit Neurosci 3:71–86
Etemad K, Chellappa R (1997) Discriminant analysis for recognition of human face images. J Opt Soc Am 14:1724–1733
Phillips P, Moon H, Rizvi SA, Rauss PJ (2000) The FERET evaluation methodology for face recognition algorithms. IEEE Trans Pattern Anal Mach Intell (PAMI) 22:1090–1104
Penev P, Atick J (1996) Local feature analysis: a general statistical theory for object representation. Netw Comput Neural Syst 7:477–500
Wiskott L, Fellous J-M, Kuiger N, Von der Malsburg C (1997) Face recognition by elastic bunch graph matching. IEEE Trans PAMI 19:775–779
Ahonen T, Hadid A, Pietikäinen M (2004) Face recognition with local binary patterns. In: Proceedings of the 8th European conference on computer vision (ECCV)
Hadid A, Pietikäinen M (2004) Selecting models from videos for appearance-based face recognition. In: Proceedings of the 17th international conference on pattern recognition (ICPR)
Pope AR, Lowe DG (2000) Probabilistic models of appearance for 3-D object recognition. IJCV 40(2):149–167
Bart E, Byvatov E, Ullman S (2004) View-invariant recognition using corresponding object fragments. In: ECCV, pp 152–165
Nanni L, Brahnam S, Lumini A (2012) Random interest regions for object recognition based on texture descriptors and bag of features. Expert Syst Appl 39:973–977
Déniz O, Bueno G, Salido J, De la Torre F (2011) Face recognition using histograms of oriented gradients. Pattern Recognit Lett 32:1598–1603
Yu J, Qin Z, Wan T, Zhang X (2013) Feature integration analysis of bag-of-features model for image retrieval. Neurocomputing 120:355–364
Pranam J, Geers G (2010) IFLT based real-time framework for image matching. In: 20th International conference on pattern recognition (ICPR), pp 2242–2245
Janney P, Yu Z (2007) Invariant features of local textures—a rotation invariant local texture descriptor. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR), pp 1–7
Sun Y, Wang X, Tang X (2013) Deep convolutional network cascade for facial point detection. In: CVPR, pp 3476–3483
Luo P, Wang X, Tang X (2012) Hierarchical face parsing via deep learning. In: Proceedings of the CVPR
Donahue J, Jia Y, Vinyals O, Hoffman J, Zhang N, Tzeng E, Darrell T (2013) Decaf: a deep convolutional activation feature for generic visual recognition. arXivpreprint arXiv:1310.1531
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: NIPS, pp 1097–1105
Zhang N, Paluri M, Ranzato M, Darrell T, Bourdev L (2014) Panda: Pose aligned networks for deep attribute modeling, In: CVPR
Zhang N, Donahue J, Girshick R, Darrell T (2014) Part-based r-cnns for fine-grained category detection. In: ECCV, pp 834–849
Oyallon E, Mallat S, Sifre L (2013) Generic deep networks with wavelet scattering. arXiv preprint arXiv:1312.5940
Wu D, Wu J, Zeng R, Jiang L, Senhadji L, Shu H (2015) Kernel principal component analysis network for image classification. arXiv preprint arXiv:1512.06337
Yanga X, Liu W, Tao D, Cheng J (2017) Canonical correlation analysis networks for two-view image recognition. Inf Sci 385(C):338–352. doi:10.1016/j.ins.2017.01.011
Parkhi OM, Vedaldi A, Zisserman A (2015) Deep face recognition. In: Proceedings of the British machine vision conference (BMVC), Swansea
Taigman Y, Yang M, Ranzato M, Wolf L (2014) Deep-face: closing the gap to human-level performance in face verification. In: Proceedings of CVPR
Huang GB, Ramesh M, Berg T, Miller EL (2007) Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical Report 07–49. University of Massachusetts, Amherst
Wolf L, Hassner Tal, Maoz I (2011) Face recognition in unconstrained videos with matched background similarity. In: Proceedings of the CVPR
Sun Y, Ding L, Wang X, Tang X (2015) Deepid3: Face recognition with very deep neural networks. In: CoRR. arXiv:abs/1502.00873
Chen D, Cao X, Wang L, Wen F, Sun J (2012) Bayesian face revisited: a joint formulation. In: Proceedings of the ECCV, pp 566–579
Sun Y, Chen Y, Wang X, Tang X (2014) Deep learning face representation by joint identification-verification. In: NIPS
Sun Y, Wang X, Tang X (2014) Deep learning face representation from predicting 10,000 classes. In: Proceedings of the CVPR
Sun Y, Wang X, Tang X (2014) Deeply learned face representations are sparse, selective, and robust. In: CoRR. arXiv:abs/1412.1265
Schroff F, Kalenichenko D, Philbin J (2015) Facenet: a unified embedding for face recognition and clustering. In: Proceedings of the CVPR
Mikolajczyk K, Schmid C (2002) An affine invariant interest point detector. In: Proceedings of the 7th European conference on computer vision, Copenhagen, Denmark, vol. I, pp 128–142
Roth D, Yang M, Ahuja N (2000) A snow-based face detector. In: Neural information processing systems
Sung KK, Poggio T (1998) Example-based learning for view-based human face detection. IEEE Trans PAMI 20:39–51
Schneiderman H, Kanade T (1998) Probabilistic modeling of local appearance and spatial relationships for object recognition. In: Proceedings of the computer vision and pattern recognition (CVPR), pp 45–51
Rowley HA, Baluja S, Kanade T (1998) Neural network-based face detection. IEEE Trans Pattern Anal Mach Intell (PAMI) 20(1):23–38
Osuna E, Freund R, Girosit F (1997) Training support vector machines: an application to face detection. In: Proceedings of the computer vision and pattern recognition (CVPR), pp 130–136
Vapnik V (1998) Statistical learning theory. Wiley, New York
Farfade SS, Saberian M, Li LJ (2015) Multi-view face detection using deep convolutional neural networks. In: Computer vision and pattern recognition. arXiv:1502.02766
Mikolajczyk K, Schmid C (2001) Indexing based on scale invariant interest points. In: Proceedings of the 8th international conference on computer vision, Vancouver, Canada, pp 525–531
Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the em algorithm. J R Stat Soc (Series B) 39(1):1–38
Gupta MR, Chen Y (2010) Theory and use of the EM algorithm. Found Trends Signal Process 4(3):223–296
Liu C (2003) A bayesian discriminating features method for face detection. IEEE Trans Pattern Anal Mach Intell 25:725–740
Elad M, HelOr Y, Keshet R (2002) Rejection based classifier for face detection. Pattern Recognit Lett 23:1459–1471
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Taffar, M., Miguet, S. Local appearance modeling for objects class recognition. Pattern Anal Applic 22, 439–455 (2019). https://doi.org/10.1007/s10044-017-0639-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10044-017-0639-2