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FACE DETECTION USING HSV COLOR SEGMENTATION AND MATCHED FILTERING

FACE DETECTION USING HSV COLOR SEGMENTATION AND MATCHED FILTERING. EE 368 DIGITAL IMAGE PROCESSING FINAL PROJECT PRESENTATION 30 MAY 2002 GROUP MEMBERS: Vey Sern Ling Scott Payne. INTRODUCTION. OVERVIEW Project Goals System Block Diagram FACE DETECTION SUB-ROUTINES

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FACE DETECTION USING HSV COLOR SEGMENTATION AND MATCHED FILTERING

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  1. FACE DETECTION USING HSV COLOR SEGMENTATION AND MATCHED FILTERING EE 368 DIGITAL IMAGE PROCESSING FINAL PROJECT PRESENTATION 30 MAY 2002 GROUP MEMBERS: Vey Sern Ling Scott Payne

  2. INTRODUCTION • OVERVIEW • Project Goals • System Block Diagram • FACE DETECTION SUB-ROUTINES • Skin Color Segmentation • Matched Filtering • FINAL TEST RESULTS • CONCLUSIONS

  3. PROJECT GOALS • MAXIMIZE ACCURATE FACE DETECTIONS • Color Image • Full, upright, and front-facing • MINIMIZE FALSE DETECTIONS • USE TOPICS/TECHNIQUES PRESENTED IN EE 368 DIGITAL IMAGE PROCESSING

  4. GROUPING, REGION REMOVAL & MORPHOLOGICAL PROCESSING (Fill & Open) INPUT SKIN COLOR SEGMENTATION USING HSV COLOR SPACE IMAGE BINARY MASK LOCATION AND SIZE OF PIXEL GROUPS OUTPUT FACE/NON-FACE DECISION NORMALIZED PEAK CORRELATION VALUE MATCHED FILTER CORRELATOR WITH CLUTTER SUPPRESSION COORDINATES OF FACE REGIONS SYSTEM OVERVIEW

  5. SKIN COLOR SEGMENTATION • DISTRIBUTION OF SKIN COLORS IN HUE, SATURATION, VALUE (HSV) COLOR SPACE SAMPLE TRAINING IMAGE FACES FACE COLOR DISTRIBUTION IN HSV COLOR SPACE (7 TRAINING IMAGES)

  6. SKIN COLOR SEGMENTATION • INITIAL SKIN COLOR SEGMENTATION PROCESS INPUT IMAGE BINARY MASK

  7. SKIN COLOR SEGMENTATION • MORPHOLOGICAL PROCESSING • Region removal based on group area and aspect ratio • Fill • Opening BINARY MASK AFTER PROCESSING BINARY MASK

  8. CLUTTER SUPPRESSION AUTOCORRELATION MATRIX OF CLUTTER PSD OF CLUTTER

  9. MATCHED FILTERING CORRELATION 1 • EVALUATE PEAK CORRELATION VALUE BETWEEN ‘AVERAGE’ FACE AND POTENTIAL FACE REGIONS MATCHED FILTERING CORRELATION WITH CLUTTER SUPPRESSION AND MAGINIFICATION SWEEP AVERAGE FACE1 2-D NORMALIZED CORRELATION MATRIX POTENTIAL FACE REGION [1. R. Frischholz, Face Detection Homepage, http://home.t-online.de/home/robert.frischholz/face.htm, May 2002]

  10. MATCHED FILTERING CORRELATION 2 • EVALUATE PEAK CORRELATION VALUE BETWEEN ‘AVERAGE’ FACE AND POTENTIAL FACE REGIONS MATCHED FILTERING CORRELATION WITH CLUTTER SUPPRESSION AND MAGINIFICATION SWEEP AVERAGE FACE1 2-D NORMALIZED CORRELATION MATRIX NON-FACE REGION WITH SKIN COLORS

  11. MATCHED FILTERING CORRELATION 3 • EVALUATE PEAK CORRELATION VALUE BETWEEN ‘AVERAGE’ FACE AND POTENTIAL FACE REGIONS MATCHED FILTERING CORRELATION WITH CLUTTER SUPPRESSION AND MAGINIFICATION SWEEP AVERAGE FACE1 2-D NORMALIZED CORRELATION MATRIX HIGH CONTRAST NON-FACE REGION (POTENTIAL FALSE DETECTION)

  12. FINAL TEST RESULTS • ANALYZED ALGORITHM PERFORMANCE USING SEVEN TRAINING IMAGES NUMBER OF HITS NUMBER OF FALSE DETECTIONS

  13. FINAL TEST RESULTS NO FALSE DETECTIONS HIGH RATE OF FALSE DETECTIONS

  14. SKIN COLOR SEGMENTATION PAIRED WITH MATCHED FILTERING • Detects faces well (93% detection rate in training images) • Quick run-time (average of 36 sec) • High variance in false detection rate • BETTER MODEL OF ‘CLUTTER’ MAY REDUCE FALSE DETECTIONS • ALGORITHM LIMITATIONS • Color images only • Full, frontal, upright faces CONCLUSIONS

  15. The End

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