8/11/2019 Temporal Correlation and Probabilistic Prediction Based Face Detection Framework in Real Time Environment

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TEMPORAL CORRELATION ANDPROBABILISTIC PREDICTION

BASED FACE DETECTION

FRAMEWORK

IN REAL TIME ENVIRONMENTDepartment of Electronics & Electrical Communication

EngineeringIndian Institute of Technology, Kharagpur

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TEAM

Indian Institute of Technology, Kharagpur

Piyush Mayank, Undergraduate Student, IIT

Kharagpur

Sudipta Mukhopadhyay, Assistant Professor, IIT

Kharagpur

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BASIC FACE DECTECTION FRAMEWORK

FACE DETECTION VIOLA JONES Face

Detection Algorithm

Integral Image Representation

AdaBoost Algorithm for creating Strong ClassifiersCascading of Classifiers

Indian Institute of Technology, Kharagpur

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IMPROVEMENT IN DETECTION

ALGORITHM

Increase Detection Rate

Reduce Computational Complexity

Decrease False Detection Rate

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PROPOSED ALGORITHMS

Indian Institute of Technology, Kharagpur

Search Area Reduction Using Probabilistic

Prediction

Minimum Face Size Optimization

False Detection Removal Using Facial Features

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SEARCH AREA REDUCTION

Any Rectangle in an image can be determined

by two diagonal coordinates:

Use of Detected Face Rectangles of previousframes

Window Size (WS) of i previous frames. Lets

say i=3 Indian Institute of Technology, Kharagpur

(x1,y1)

(x2,y2)

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SEARCH AREA REDUCTION

Indian Institute of Technology, Kharagpur

(x11,y11)

(x21,y21)

(x12,y12)

(x12,y12)

(x13,y13)

(x23,y23)

x

y

x1min

y1min

x2max

y2max

(x1min, y1min)

(x2max, y2max)

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SEARCH AREA REDUCTION

Probability of existence does not terminate

abruptly outside this area.

In Gaussian Distribution

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SEARCH AREA REDUCTION

Indian Institute of Technology, Kharagpur

x

y

(x1min, y1min)

(x2max, y2max)

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MINIMUM FACE SIZE OPTIMIZATION

To define the minimum size of a face which will be

searched in the reduced search area.

Movement of face in frames bear a temporal correlation

Use of detected face rectangles (DFR) of previousframes

Window Size of i frames

Minimum Face Width (MFW) and Length (MFL)

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WORST CASE SCENARIO

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FALSE DETECTION REMOVAL

Structural Facial Features, the two eyes, nose and

mouth are detection after successful detection of a

face

The face detection is confirmed only if at least oneof these features are detected otherwise the

detection is rejected

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IMPLEMENTATION

Indian Institute of Technology, Kharagpur

Four Input Sequences:

Input 1: JEVM392_aux_kaumuskel_CamD.mpg

(Regulated Input)

Input 2: Real time Regulated Input from Camera Input 3: Behzad.avi (Unregulated Input)

Input 4: Real Time Unregulated Input from Camera

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IMPLEMENTATION

PERFORMANCE METRICS

Detection Rate (in %)

Frame Rate Per Second ( in sec-1)

Worst Case Complexity (in msec)

COMPARED FRAMEWORKS

VIOLA JONES FACE DETECTION

PROPOSED ALGORITHMS WITH WINDOW

SIZE=3,5,7

Indian Institute of Technology, Kharagpur

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RESULTS

Viola Jones

FDF

FDF with

WS=3

FDF with

WS=5

FDF with

WS=7

Detection Rate (in %) 100 100 100 100

Frame Rate per second (in sec1 ) 26.15 26.29 26.33 26.32

Worst Case Complexity (inmsec) 99.278 47.235 47.185 47.211

Indian Institute of Technology, Kharagpur

Viola Jones

FDF

FDF with

WS=3

FDF with

WS=5

FDF with

WS=7

Detection Rate (in %) 96 100 99.2 99.6

Frame Rate per second (in sec1 ) 18.375 21.16 22.05 21.78

Worst Case Complexity (inmsec) 125.065 97.63 78.78 94.293

INPUT

1

INPUT

2

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RESULTS

Viola Jones

FDF

FDF with

WS=3

FDF with

WS=5

FDF with

WS=7

Detection Rate (in %) 58 67.5 68.2 67

Frame Rate per second (in sec1 ) 20.03 34.05 36.04 23.2

Worst Case Complexity (inmsec) 56.962 46.678 46.085 46.556

Indian Institute of Technology, Kharagpur

Viola Jones

FDF

FDF with

WS=3

FDF with

WS=5

FDF with

WS=7

Detection Rate (in %) 83 95.6 92.4 98.5

Frame Rate per second (in sec1 ) 18.85 21.96 23.15 19.36

Worst Case Complexity (inmsec) 172.363 132.23 136.63 165.43

INPUT

3

INPUT

4

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RESULTS

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RESULTS

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RESULTS

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CONCLUSION

Striking Analogy Between Real Time Face Detection and

Video Motion Estimation

Adaptive Search using Probabilistic Prediction

Window Size is not increased beyond 7 as it would leadto high increase in computational complexity. Tradeoff

Between Computational Efficiency and Detection

Efficiency

Window Size of 5 was found to be most optimal

System currently works only for frontal faces. Inclusion

of Profile/Lateral Faces Desirable but not necessary.

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THANK YOU

Indian Institute of Technology, Kharagpur