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Face Detection: a Survey Speaker: Mine-Quan Jing National Chiao Tung University
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Outline Application Related techniques Segmentation Identification Recognition Progress ( 目前進展 ) Systems Demo NTU,NCTU,NTHU,ACADMIA SINICA
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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
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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.
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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
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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. 1. Label the edge 2. 2. Matched to a face model 3. 3. Golden ratio
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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
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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
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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]
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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
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Low-Level Analysis: Segmentation of visual features The disadvantage: Not robust under varying lighting condiction
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Color based segmentation: Skin model construction (Example) The original image was taken from http://nn.csie.nctu.edu.tw/face-detection/ppframe.htm
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Color based segmentation: Skin model construction (Example) The original image was taken from http://nn.csie.nctu.edu.tw/face-detection/ppframe.htm
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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 1. Frame differencing 2. Thresholding 3. Noise removal 4. Locate the face http://ansatte.hig.no/~erikh/papers/hig98_6/node2.html#bevdet
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Related techniques – Change Detector A typical motion image 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
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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.
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The face detection techniques Image-Based Approach Linear Subspace Methods Neural Networks Statistical Approaches
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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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