Eyes detection in compressed domain using Eyes detection in compressed domain using classificationclassification

Eng. Alexandru POPA

alexandru_popa@autenticmedia.com

Technical University of Cluj-Napoca

Faculty of Electronics, Telecommunications and Information Technology

Object detection in digital imagesThe principle of image processing in the compressed domainThe Discrete Cosine Transform (DCT)The spatial relationship of DCT coefficients between a block and its sub-blocks Object recognition using classificationThe linear discriminant classifier (LDA, Fisher classifier)DemoResultsConclusions

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the approached method consists in feature extraction using image transformations, creation of a new space of features followed by objects classification in that space

feature extraction methods: DCT, Wavelet, Gabor

DCT gives in general good features for object description. Is the base of the JPEG standard, and the properties of the DCT coefficients blocks, makes them very good for generating features spaces

the idea is to make the classification of the objects direct in JPEG compressed domain

DCT = Discrete Cosine Transform

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almost all image processing algorithms are defined in pixel level; rewriting them in the compressed domain is not directstandard implementation schemes decompress the image, apply the algorithm and them recompress the image. The disadvantage is that these schemes are time consumingit is wished to rewrite these algorithms directly in the compressed domain for optimizing the processing chain

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E n tr o p yd ec o d e

D ec o m p r es s io n( R L E , Z ig - zag ,

Q u an tizer , D C T )

P ix e l lev e lp r o c es s in g

C o m p r es s ed d o m ainp r o c es s in g

C o m p r es s io n( R L E , Z ig - zag ,

Q u an tizer , D C T )R L E

v ec to r

E n tr o p yen c o d e

J P E Gb its tr eam

R L Ev ec to r

J P E Gb its tr eam

The formula for DCT applied on a image:

Properties:

Decorelation – the principal advantage of transformed images is the low redundancy between neighbours pixels. From this fact results uncorrelated coefficients which can be coded independently Energy compactness – the capacity of the transformation to pack the input datas in as few coefficients as possible Separability – the 2D DCT can be calculated in two steps by applying the 1D formula successively on the lines and the columns of an image

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(1)

(2)

a new problem could occur from the fact that various DCT block sizes have to be used in order to ensure optimized performances8x8 blocks used in JPEG, 4x4 blocks used in image indexing, and 16x16 macro-blocks in MPEGto deal with inter-transfer of DCT coefficients from different blocks with various sizes, the existing approach would have to decompress the pixel data in the spatial domain via the IDCT, redivide the pixels into new blocks with the required size and then apply the DCT again to produce the DCT coefficientsit is obvious that the approach is inefficient

Bibliography: The Spatial Relationship of DCT Coefficients Between a Block and Its Sub-blocks, Jianmin Jiang and Guocan Feng

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4x4 block

Transformation from 4 blocks of 2x2 pixels in one of 4x4 pixels:

106 97 83 85

106 95 84 85

105 84 74 69

77 60 57 89

The block with the pixelsluminance

DCT202 10 168 -1

1 -1 84 85

163 19 144 -13

26 2 -1 19

The DCT coefficients of4 block of 2x2 pixels

Matricea A*

339 23 22 -3

34 13 -13 0

-12 -16 8 -5

-1 13 -4 4

1 0 1 0

0.9239 0.3827 -0.9239 0.3827

0 1 0 -1

-0.3827 0.9239 0.3827 0.9239

Ecuation:

Original image

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(3)

Transformation form a 4x4 block to 4 block of 2x2 pixels:

106 97 83 85

106 95 84 85

105 84 74 69

77 60 57 89

DCT

202 10 168 -1

1 -1 84 85

163 19 144 -13

26 2 -1 19

The inverse matrix of A*

339 23 22 -3

34 13 -13 0

-12 -16 8 -5

-1 13 -4 4

0.5 0.4619 0.5 -0.1913

0 0.1913 0 0.4619

0.5 -0.4619 0.5 0.1913

0 0.1913 0 0.4619

Ecuation :

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(4)

The block with the pixelsluminance

The DCT coefficients ofThe 4x4 block

Original image 4x4 block

geometric classifiers are those classifiers which implies the deduction of some decision borders in the features spacea classifier demands a set of training datas (datas + labels)the number of datas must be big enough for a correct learning with generalization capacity for unknown datas

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Data classification:

means that an unknown sample is presented to the classifier, his position regarding the decision boundaries is calculated and depending on it a label is associated

LDA (Linear Discriminant Analysis) using Fisher’s classifier implies finding a line in the features space and projecting the datas from the training set on this line. Describes the datas by their projectionsConsidering a bi-dimensional space we have:

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Fisher’s criteria for selecting w and w0 parameters:

The optimal direction w is the line direction for which: 1) the distance between the projections of the classes centers on w is maximum2) the variance of the projections from each class is minimumThe optimum value w0 is the scalar value which minimize the classification error in the training data set

is the label assigned to the i data by the Fisher classifier

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The image form which the training set was taken

it was proved that the implementation of Fisher`s classifier in compressed domain was a wise choice because it has good results in eyes regions detectionit`s a novelty in the image processing field because this algorithm wasn`t written in compressed domainusing the spatial relationship of DCT coefficients between a block and its sub-blocks facilitates the computation of coefficients for big blocks starting from small blocks in the way of speed and computation complexity

Others applications that can derive:

gaze tracking/focusing

automatic system for detecting the vigilance of driversbiometrics applications: person identification using iris recognition

, contează foarte mult structura acesteia precum şi setul de antrenare

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Thank you for your Thank you for your attention!attention!Questions?Questions?

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