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  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

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.

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

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 Label the edge Matched to a face model Golden ratio

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

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

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]

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

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

Color based segmentation: Skin model construction (Example) The original image was taken from

Color based segmentation: Skin model construction (Example) The original image was taken from

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

Related techniques – Change Detector A typical motion image Amount of pixels on each line in the motion image The original images were taken from

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.

The face detection techniques Image-Based Approach  Linear Subspace Methods  Neural Networks  Statistical Approaches

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