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Face Detection: a Survey

Face Detection: a Survey. Speaker: Mine-Quan Jing National Chiao Tung University. Outline. Application Related techniques Segmentation Identification Recognition Progress ( 目前進展 ) Systems Demo NTU,NCTU,NTHU,ACADMIA SINICA. The face detection techniques. Feature-Based Approach

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Face Detection: a Survey

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  1. Face Detection: a Survey Speaker: Mine-Quan Jing National Chiao Tung University

  2. Outline • Application • Related techniques • Segmentation • Identification • Recognition • Progress (目前進展) • Systems Demo • NTU,NCTU,NTHU,ACADMIA SINICA

  3. The face detection techniques • Feature-Based Approach • Skin color and face geometry • Detection task is accomplished by • Distance, angles and area of visual features • Image-Based Approach • As a general recognition system

  4. The face detection techniques • Feature-Based Approach • Low-Level Analysis • Segmentation of visual features • Feature Analysis • Organized the features into • 1. Global concept • 2. Facial features • Active Shape Models • Extract the complex & non-rigid feature Ex: eye pupil, lip tracking.

  5. Low-Level Analysis:Segmentation of visual features • Edges: (The most primitive feature) • Trace a human head outline. • Provide the information • Shape & position of the face • Edge operators • Sobel • Marr-Hildreth • first and second derivatives of Gaussians

  6. Low-Level Analysis:Segmentation of visual features • The steerable filtering 1. Detection of edges 2. Determining the orientation 3. Tracking the neighboring edges • Edge-detection system • 1. Label the edge • 2. Matched to a face model • 3. Golden ratio

  7. Low-Level Analysis:Segmentation of visual features • Gray information • Facial feature ( eyebrows , pupils …) • Application • Search an eye pair • Find the bright pixel (nose tips) • Mosaic (pyramid) images Darker than their surrounding

  8. Segmentation of visual features: Color Based Segmentation • Color information • Difference races? • Different skin color gives rise to a tight cluster in color space. • Color models • Normalized RGB colors • A color histogram for a face is made • Comparing the color of a pixel with respect to the r and g. Why normalized ? Brightness change

  9. Low-Level Analysis:Segmentation of visual features • HSI color model • For large variance among facial feature clusters [106]. • Extract lips, eyes, and eyebrows. • Also used in face segmentation • YIQ • Color’s ranging from orange to cyan • Enhance the skin region of Asians [29]. • Other color models • HSV, YES, CIE-xyz … • Comparative study of color space [Terrilon 188]

  10. Low-Level Analysis:Segmentation of visual features • Color segmentation by color thresholds • Skin color is modeled through • Histogram or charts (simple) • Statistical measures (complex) • Ex: • Skin color cluster can be represented as Gaussian distribution [215] • Advantage of Statistical color model • The model is updatable • More robust against changes in environment

  11. Low-Level Analysis:Segmentation of visual features • The disadvantage: • Not robust under varying lighting condiction

  12. Color based segmentation:Skin model construction (Example) The original image was taken from http://nn.csie.nctu.edu.tw/face-detection/ppframe.htm

  13. Color based segmentation:Skin model construction (Example) The original image was taken from http://nn.csie.nctu.edu.tw/face-detection/ppframe.htm

  14. Low-Level Analysis:Segmentation of visual features • Motion information • a face is almost always moving • Disadvantages: • What if there are other object moving in the background. • Four steps for detection • Frame differencing • Thresholding • Noise removal • Locate the face http://ansatte.hig.no/~erikh/papers/hig98_6/node2.html#bevdet

  15. A typical motion image Related techniques –Change Detector Amount of pixels on each line in the motion image The original images were taken from http://ansatte.hig.no/~erikh/papers/hig98_6/node2.html#bevdet

  16. Motion-Based segmentation: • Motion estimation [126] • People are always moving. • For focusing of attention • discard cluttered, static background • A spatio-temporal Gaussian filter can be used to detect moving boundaries of faces.

  17. The face detection techniques • Image-Based Approach • Linear Subspace Methods • Neural Networks • Statistical Approaches

  18. Related News • The 5th International Conference on Automatic Face and Gesture Recognition will take place 2002 in Washington D.C., USA.

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