Analyzing the influence of contrast in large-scale recognition of natural images

Abstract

This paper analyzes both the isolated influence of illumination quality in 2D facial recognition and also the influence of contrast measures in large-scale recognition of low-resolution natural images. First, using the Yale Face Database B, we have shown that by separately estimating the illumination quality of facial images (through a fuzzy inference system that combines average brightness and global contrast of the patterns) and by recognizing the same images using a multilayer perceptron, there exists a nearly-linear correlation between both illumination and recognition results. Second, we introduced a new contrast measure, called Harris Points Measured Contrast (HPMC), which assigns values of contrast in a more consistent form to images, according to their recognition rate than other global and local compared contrast analysis methods. For our experiments on image contrast analysis, we have used the CIFAR-10 dataset with 60,000 images and convolutional neural networks as classification models. Our results can be considered to decide if it is worth using a given test image, according to its calculated contrast applying the proposed HPCM metric, for further recognition tasks.

Keywords

Image quality assessment contrast measures multilayer perceptron convolutional neural network 2D face images CIFAR-10 images

References

Adini

, Moses

and Ullman

, Face recognition: The problem of compensating for changes in illumination direction, IEEE Trans, Pattern Analysis and Machine Intelligence 19 (1997), 721-732.

Ahumada

A.J.

and Beard

B.L.

, A Simple Vision Model for Inhomogeneous Image Quality Assessment, In: SID Digest of Technical Papers, vol. 29, 1998, pp. 1109-1111.

Azad

, Asfour

and Dillmann

, Combining Harris interest points and the SIFT descriptor for fast scale-invariant object recognition, Proc. IEEE/RSJ international conference on Intelligent robots and systems, 2009, pp. 4275-4280.

Bengio

, Learning Deep Architectures for AI, Foundations and Trends in Machine Learning 2 (2009), 1-127.

Bergstra

, Breuleux

, Bastien

, Lamblin

, Pascanu

, Desjardins

, Turian

, Warde-Farley

and Bengio

, Theano: A CPU and GPU Math Expression Compiler, Proc. Of Python for Scientific Computing Conference (SciPy), 2010, pp. 1-7.

Calabria

A.J.

and Fairchild

M.D.

, Perceived image contrast and observer preference II. Empirical modeling of perceived image contrast and observer preference data, The Journal of Imaging Science and Technology 47 (2003), 494-508.

Deng

, Dong

, Socher

, Li

L.-J.

, Li

and Li

F.-F.

, ImageNet: A Large-Scale Hierarchical Image Database, Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR'09), 2009, pp. 248-255.

Dieleman

, Tutorial on Deep Learning & Theano, 2015.

Deng

and Yu

, Deep Learning: Methods and Applications, Foundations and Trends in Signal Processing Archive 7 (2014), 197-387.

10.

Fergus

, Perona

and Zisserman

, Object class recognition by unsupervised scale-invariant learning, Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR'03) 2 (2003), 264-271.

11.

Georghiades

, Belhumeur

P.N.

and Kriegman

, From Few to Many: Illumination Cone Models for Face Recognition under Variable lighting and Pose, IEEE Trans. Pattern Analysis and Machine Intelligence 23 (2001), 643-660.

12.

Gonzalez

R.C.

and Woods

R.E.

, Digital Image Processing, Third Edition, Prentice Hall, 2008.

13.

Gross

, Baker

, Matthews

and Kanade

, Face Recognition across Pose and Illumination, in: Handbook of Face Recognition, Li

S.Z.

and Jain

A.K.

, eds, Springer-Verlag, 2004, pp. 193-216.

14.

, Zhai

, Yang

, Zhang

and Liu

, Subjective and objective quality assessment for images with contrast change, Proc. 20th IEEE International Conference Image Processing (ICIP'13), 2013, pp. 383-387.

15.

Hoffmann

, CIELab Color Space, PDF Document, 2003. URL: http://docs-hoffmann.de/cielab03022003.pdf.

16.

Hou

, Chen

, Qin

and Zhang

, Automatic image search based on improved feature descriptors and decision tree, Integrated Computer-Aided Engineering 18(2) (2011), 167-180.

17.

Krizhevsky

, Learning multiple layers of features from tiny images. Technical Report, 2009. URL: http://www.cs.toronto. edu/∼kriz/learning-features-2009-TR.pdf.

18.

Krizhevsky

, Sutskever

and Hinton

G.E.

, Imagenet classification with deep con volutional neural networks, in: Advances in neural information processing systems, 2012, pp. 1097-1105.

19.

Larson

E.C.

and Chandler

D.M.

, Most apparent distortion: full-reference image quality assessment and the role of strategy, Journal of Electronic Imaging 19(1) (2010).

20.

LeCun

, Bottou

, Bengio

and Haffner

, Gradient-Based Learning Applied to Document Recognition, Proceedings of the IEEE 86 (1998), 2278-2324.

21.

LeCun

, Bengio

and Hinton

, Deep learning, Nature 521 (2015), 436-444.

22.

, Wang

and Ao

, Illumination Processing in Face Recognition. In: Oravec

(ed.): Face Recognition, InTech. 2010. URL: http://www.intechopen.com/books/face-recognition/illumination-processing-in-face-recognition.

23.

Lin

and Kuo

C.C.-J.

, Perceptual visual quality metrics: A survey, Journal of Visual Communication and Image Representation 22 (2011), 297-312.

24.

Lin

, Lv

, Zhu

, Yang

, Cour

, Yu

, Cao

and Huang

, Large-scale Image Classification: Fast Feature Extraction and SVM Training, Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR'11), 2011, pp. 1689-1696.

25.

Lisin

D.A.

et al., Combining Local and Global Image Features for Object Class Recognition, Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR'05) 3 (2005), 47-54.

26.

McCann

J.J.

and Rizzi

, The Art and Science of HDR Imaging. John Wiley & Sons, 2012.

27.

Michelson

, Studies in Optics, University of Chicago Press, 1927.

28.

Netzer

, Wang

, Coates

, Bissacco

, Wu

and Ng

A.Y.

, Reading Digits in Natural Images with Unsupervised Feature Learning, NIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2011, pp. 1-9.

29.

Ohayon

, Freiwald

W.A.

and Tsao

D.Y.

, What makes a cell face selective? The importance of contrast, Neuron 74 (2013), 567-581.

30.

Pedersen

, Bonnier

, Hardeberg

J.Y.

and Albregtsen

, Attributes of image quality for color prints, Journal of Electronic Imaging 19 (2010), 011016-1-011016-13.

31.

Peli

, Contrast in complex images, Journal of the Optical Society of America 7 (1990), 2032-2040.

32.

Rahman

, Jobson

D.J.

and Woodell

G.A.

, Multi-scale retinex for color image enhancement, Proc. IEEE International Conference on Image Processing (ICIP'96) 3 (1996), 1003-1006.

33.

Reinhard

, Ward

, Pattanaik

and Debevec

, High Dynamic Range Imaging - Acquisition, Display and Image-Based Lighting, Morgan Kaufmann Publisher, 2005.

34.

Rizo-Rodrıguez

, Mendez-Vazquez

and Garcıa-Reyes

, An Illumination Quality Measure for Face Recognition, Proc. 20th International Conference on Pattern Recognition (ICPR'10), 2010, pp. 1477-1480.

35.

Rizzi

, Algeri

, Medeghini

and Marini

, A proposal for contrast measure in digital images, Proc. Second European Conference on Color in Graphics, Imaging, and Vision (CGIV'04), 2004, pp. 187-192.

36.

Ruiz del Solar

and Quinteros

, Illumination compensation and normalization in eigenspace-based face recognition: A comparative study of different pre-processing approaches, Pattern Recognition Letters 29 (2008), 1966-1979.

37.

Saitoh

, Image contrast enhancement using genetic algorithm, Proc. 1999 IEEE International Conference on Systems, Man, and Cybernetics (SMC'99) 4 (1999), 899-904.

38.

Sanchez

, Velez

J.F.

and Moreno

A.B.

, On the Influence of Illumination Quality in 2D Facial Recognition, in: Bioinspired Computation in Artificial Systems, LNCS 9108, 2015, pp. 79-87.

39.

Simone

, Pedersen

and Hardeberg

J.Y.

, Measuring perceptual contrast in digital images, Journal of Visual Communication and Image Representation 23 (2012), 491-506.

40.

Sugeno

, Industrial Applications of Fuzzy Control, Elsevier Publishing Company, 1985.

41.

Szeliski

, Computer Vision: Algorithms and Applications, Springer, 2010.

42.

Tadmor

and Tolhurst

D.J.

, Calculating the contrast of retinal ganglion cells and LGN neurons encounter in natural scenes, Vision Research 40 (2000), 3145-3157.

43.

Tran

Q.D.

and Liatsis

, Improving Fusion with Optimal Weight Selection in Face Recognition, Integrated Computer-Aided Engineering 19(3) (2012), 229-237.

44.

Turk

and Pentland

, Face recognition using eigenfaces, Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR '91), 1991, pp. 586-591.

45.

Wan

, Zeiler

, Zhang

, LeCun

and Fergus

, Regularization of neural networks using dropconnect, Proc. 30th International Conference on Machine Learning (ICML'13), 2013, pp. 1058-1066.

46.

Wang

, Bovik

A.C.

, Sheikh

H.R.

and Simoncelli

E.P.

, Image Quality Assessment: From Error Visibility to Structural Similarity, IEEE Trans. Image Processing 13 (2004), 600-612.

47.

Whittle

, Increments and decrements: luminance discrimination, Vision Research 26 (1986), 1677-1691.

48.

Wolf

, Jolion

, Kropatsch

and Bischof

, Content based image retrieval using interest points and texture features, Proc. 15th International Conference on Pattern Recognition 4 (2000), 234-237.

49.

and McKelvey

, A Robust Subspace Classification Scheme Based on Empirical Intersection Removal and Sparse Approximation, Integrated Computer-Aided Engineering 22(1) (2015), 59-69.

50.

Zeiler

M.D.

and Fergus

, Stochastic pooling for regularization of deep convolutional neural networks, Proc. International Conference on Learning Representations, 2013.

51.

Zhang

and Xie

, A study on the effective approach to illumination-invariant face recognition based on a single image, Proc. 7th Chinese Conference on Biometric Recognition (CCBR'12), LNCS 7701, Springer, 2012, pp. 33-41.

52.

Zhang

, Yan

M.Y.

and Zeng

Y.J.

, Fatigue Detection with 3D Facial Features based on Binocular Stereo Vision Technology, Integrated Computer-Aided Engineering 21(4) (2014), 387-397.

53.

Zou

, Kittler

and Messer

, Illumination Invariant Face Recognition: A Survey, Proc. First IEEE Intl. Conf. on Biometrics: Theory, Applications and Systems (BTAS'07), 2007, pp. 1-8.