Automated Face Automated Face DetectionDetection

Peter BrendePeter Brende

David Black-SchafferDavid Black-Schaffer

Veni BourakovVeni Bourakov

Primary ChallengesPrimary Challenges

Scale differences Scale differences Overlapping/obstructed facesOverlapping/obstructed faces Lighting variationLighting variation

Implementation OverviewImplementation Overview

1.1. Color-based skin separationColor-based skin separation

2.2. Spatial analysis to generate candidate facesSpatial analysis to generate candidate faces Morphological?Morphological? or Template Matching?or Template Matching?

3.3. Eliminate false hitsEliminate false hits Hands & arms, based on shapeHands & arms, based on shape Roof, based on textureRoof, based on texture Neck, based on relative positionNeck, based on relative position

Color-based Skin SeparationColor-based Skin Separation

What color space to use?What color space to use?

How to separate out the skin color?How to separate out the skin color?

Marginal Color DistributionsMarginal Color Distributions

Parametric SeparationParametric Separation

Simple & Fast (h>0.98 or h<0.01)Simple & Fast (h>0.98 or h<0.01) Problems with non-linearity of HSV space in bright areasProblems with non-linearity of HSV space in bright areas

Full Joint-Probability DistributionFull Joint-Probability Distribution

We have enough data, so why not?We have enough data, so why not?

Provides most accurate per-pixel Provides most accurate per-pixel classification. classification.

Allows use to circumvent choosing a decision Allows use to circumvent choosing a decision boundary. We can simply use Bayes rule.boundary. We can simply use Bayes rule.

Slices from 3D Joint DistributionSlices from 3D Joint Distribution

Skin-probability Image Obtained Skin-probability Image Obtained From Applying Bayes RuleFrom Applying Bayes Rule

Color-based Skin SeparationColor-based Skin Separation

What color space to use?What color space to use? HSV if separability of distributions is necessaryHSV if separability of distributions is necessary

How to separate out the skin color?How to separate out the skin color? Parametric is fast but loose in HSVParametric is fast but loose in HSV

Provides a binary mapping and requires choosing Provides a binary mapping and requires choosing thresholdsthresholds

Full PDF is accurate in any color spaceFull PDF is accurate in any color space Can be fast if done correctly (table lookup)Can be fast if done correctly (table lookup) No thresholds: produces a pure probability mapNo thresholds: produces a pure probability map

Spatial Analysis MethodSpatial Analysis Method

MorphologicalMorphological Obtain binary mask through thresholdingObtain binary mask through thresholding Perform morphological operations to separate and Perform morphological operations to separate and

identify blobs corresponding to facesidentify blobs corresponding to faces Difficult due to overlapping facesDifficult due to overlapping faces

Template MatchTemplate Match Search a scene for prototypical face imageSearch a scene for prototypical face image Need to decide which data to work with Need to decide which data to work with

(luminance vs. skin probability)(luminance vs. skin probability)

Template MatchingTemplate Matching

Using the skin-probability image:Using the skin-probability image: Greatly simplifies information contentGreatly simplifies information content

Simple information Simple information simple algorithm simple algorithm

Allows algorithm to focus on the single best facial Allows algorithm to focus on the single best facial clue: oval-shaped clue: oval-shaped skinskin regions regions

Allows us to avoid creating a binary maskAllows us to avoid creating a binary mask

Correlation of simple template Correlation of simple template with Skin-probability imagewith Skin-probability image

Process of Inclusion/EliminationProcess of Inclusion/Elimination

Iteratively pick ‘strong’ regions of the skin-Iteratively pick ‘strong’ regions of the skin-probability image as faces:probability image as faces: For each template search for matching face shapes For each template search for matching face shapes

(convolution peaks)(convolution peaks) For each detected face, ‘subtract/erase’ the region For each detected face, ‘subtract/erase’ the region

from image to avoid duplicate detectionfrom image to avoid duplicate detection Stop when no significant skin regions remainingStop when no significant skin regions remaining

Positive Detection and Positive Detection and EliminationElimination

Template and Threshold Template and Threshold SelectionSelection

Critical step of our algorithmCritical step of our algorithm Potential problems:Potential problems:

Template matching face of a different sizeTemplate matching face of a different size Small template leads to double hits laterSmall template leads to double hits later Large template leads to missed facesLarge template leads to missed faces

Bad thresholds Bad thresholds low sensitivity or low specificity low sensitivity or low specificity Solution:Solution:

Use templates of many sizes, going from largest to smallestUse templates of many sizes, going from largest to smallest Set threshold as high as possible without sacrificing Set threshold as high as possible without sacrificing

sensitivitysensitivity

Algorithm ImplementationAlgorithm Implementation

1.1. Load probability and template data Load probability and template data2.2. Down-sample the image by factor 2:1 Down-sample the image by factor 2:13.3. Calculate the face-probability image by color Calculate the face-probability image by color4.4. Remove hands/arms Remove hands/arms5.5. Template match with skin-probability image Template match with skin-probability image6.6. Eliminate false positive hits on necks of large Eliminate false positive hits on necks of large

facesfaces7.7. Remove patterned hits Remove patterned hits

Overall ResultsOverall ResultsImage 1 2 3 4 5 6 7 Total hits 21 23 25 24 23 23 22 161 misses 0 1 0 0 1 0 0 2 2nd hits 0 1 0 0 0 0 1 2 faces 21 24 25 24 24 24 22 164 Image 1 2 3 4 5 6 7 Total %hits 100% 96% 100% 100% 96% 96% 100% 98% %misses 0% 4% 0% 0% 4% 0% 0% 1% %2nd hits 0% 4% 0% 0% 0% 0% 5% 1% Image 1 2 3 4 5 6 7 Total hits 23 6 13 5 26 24 5 102 misses 3 17 10 17 1 2 17 67 2nd hits 2 0 0 0 0 2 0 4 faces 25 23 23 22 27 26 23 169 Image 1 2 3 4 5 6 7 Total %hits 92% 26% 57% 23% 96% 92% 22% 60% %misses 12% 74% 43% 77% 4% 8% 74% 40% %2ndhits 8% 0% 0% 0% 0% 8% 0% 2%

ConclusionsConclusions

Recognition of face-shaped blobs from the skin-Recognition of face-shaped blobs from the skin-probability map works excellentlyprobability map works excellently Requires that the skin colors be well knownRequires that the skin colors be well known Requires that the general face sizes be well knownRequires that the general face sizes be well known

Our set of images was relatively consistent in terms Our set of images was relatively consistent in terms of these factorsof these factors

17

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5 (0)4 (19)13

5 (0)4 (19)12

5 (0)13 (08)11

2 (1)2 (20)10

2 (1)1 (22)9

1 (2)11 (14)8

5 (0)10 (16)7

5 (0)9 (17)6

5 (0)12 (13)5

5 (0)7 (18)4

5 (0)7 (18)3

5 (0)2 (20)2

2 (1)4 (19)1

Gender RecognitionFace Detection

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16

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14

5 (0)4 (19)13

5 (0)4 (19)12

5 (0)13 (08)11

2 (1)2 (20)10

2 (1)1 (22)9

1 (2)11 (14)8

5 (0)10 (16)7

5 (0)9 (17)6

5 (0)12 (13)5

5 (0)7 (18)4

5 (0)7 (18)3

5 (0)2 (20)2

2 (1)4 (19)1

Gender RecognitionFace Detection