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By Using Statistical Models to Detect the Characteristics of Human Face 利用統計模型在彩色圖像 中偵測人臉特徵 PowerPoint Presentation
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By Using Statistical Models to Detect the Characteristics of Human Face 利用統計模型在彩色圖像 中偵測人臉特徵. 逄霖生 中國文化大學 電機工程學系. Outline. Introduction Statistical Detection Models Acquisition of Human Face Images Skin Detection Ocular Region Detection Experimental Results ROC Evaluation Conclusions

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By Using Statistical Models to Detect the Characteristics of Human Face 利用統計模型在彩色圖像 中偵測人臉特徵


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    1. By Using Statistical Models to Detect the Characteristics of Human Face利用統計模型在彩色圖像中偵測人臉特徵 逄霖生 中國文化大學 電機工程學系

    2. Outline • Introduction • Statistical Detection Models • Acquisition of Human Face Images • Skin Detection • Ocular Region Detection • Experimental Results • ROC Evaluation • Conclusions • Future Works

    3. Introduction • To Detect human face by using statistical methods • The given image is treated as an random variable. • The colors and other features of data is treated as the outcomes of the given random variable. • The prior and posterior information can be used to handle statistical data. • The uncertainty of information reveals the variations of data. • The ROC curve statistically evaluates the detection results.

    4. Statistical Detection Models • Bayes’ Filter for Skin Detection • Entropy Model for Eye Detection • ROC (Receiver Operating Characteristic) curve for statistical evaluation of skin detection results

    5. Statistical Detection Models • Bayes Rule • Entropy • ROCcurve (Receiver Operating Characteristic curve) • Statistical Evaluation of Detection Results

    6. Flow Chart Raw Image Data Skin Detection (Bayes filter) Color Conversion Eye & Eyebrow Detection (Entropy analysis) Mouth Detection (Color ratio analysis) Performance Evaluation (ROC curve) Face Detection

    7. Color Space Conversion • RGB Primary colors (tri-stimulus values of colors) • YCbCr Luminance & Chrominance • Gray Level s = T(r) where “s” is an output image, “r” is an input image

    8. Image Acquisition [Left] Original Image, [Right] Pre-selected Skin Area Note that the eye & eye brow, mouth are not part of skin

    9. Skin Detection • Applying a Bayes Filterto an image where p(x) and p(y) are pdfs of random variables x and y, p(x|y) is the posterior probability p(y|x) is the prior probability.

    10. Skin Detection

    11. Skin Detection • By using a Bayes filter and a thresholding method, the skin detection result of an image is shown as follow:

    12. Skin Detection Morphology

    13. Entropy • Entropy(熵) p(Ii) is the probability for the outcome Ii • Measure the degrees of uncertainty for different outcomes from a given random event

    14. Ocular Region Detection

    15. Lip/Mouth Detection

    16. Experimental Result

    17. ROC Curve • ROCCurve(Receiver Operating Characteristic curve) • ROC analysis provides tools to select possibly optimal models and to discard suboptimal ones independently from the cost context or the class distribution. • ROC analysis is related in a direct and natural way to cost/benefit analysis of diagnostic decision or quality making. • It is widely used in binary discrimination evaluation.

    18. Evaluation of Skin Detection TPPTrue Positive Possibility =sensitivity FNP False Negative Possibility FPP False Positive Possibility =1-specificity TNP True Negative Possibility

    19. Conclusions • Statistical methods are able to classify and detect human characteristics. • Using the prior information can help us to recognize the posterior situation. • The uncertainty of analyzed data gives the location of the area of eye. • ROC curve can determine the content of experimental results.

    20. Future Works • Adapted with Environmental Variations • Hardware Acceleration