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An Adaptive Image Enhancement Algorithm for Face Detection

An Adaptive Image Enhancement Algorithm for Face Detection. By Lizuo Jin, Shin ’ ichi Satoh, and Masao Sakauchi. ECE 738 In Young Chung. Outline. Motivation – Problem in face detection Suggestion Basic idea of suggestion Approach Adaptive Image Enhancement Algorithm

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An Adaptive Image Enhancement Algorithm for Face Detection

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  1. An Adaptive Image Enhancement Algorithm for Face Detection By Lizuo Jin, Shin’ichi Satoh, and Masao Sakauchi. ECE 738 In Young Chung

  2. Outline • Motivation – Problem in face detection • Suggestion • Basic idea of suggestion • Approach • Adaptive Image Enhancement Algorithm < Step 1. ~ Step 6. > • Result and Comparison • Conclusion

  3. HE After Histogram Equalization Problem in Face Detection? • Almost every face-detection methods depends on the intensity values of image  Face detection under unconstrained condition result in failure because of the drastic variation of pixel intensity in face regions • Image enhancement by intensity transformation can reduce this problem, with histogram equalization (HE).  HE applied to images with faces on a very light background, it may produce very dark face regions  face detection failure.

  4. Suggestion of solution • Solution? An adaptive image enhancement algorithm which is adapt to the intensity distribution within an image.

  5. Basic idea 1. Why don’t we make it even? Entropy of darker pixels = Entropy of lighter pixels 2. Face is made up of many pixels Face pixels make a cluster in histogram  We can histogram ridge analysis technique

  6. Approaches I. • EER (Entropy Error Rate) as an information theoretic measure • represents the tendency of the information distribution within an image • If EER is positive and large the information lies mainly in the darker pixels • If EER is negative large the information is lies mainly in the lighter pixels • Goal : minimizing the EER Where, S : estimate the relative position of the mean in histogram HD,HB : information in darker pixels and lighter pixels respectively HD,HB : the average entropy in either side

  7. Approaches II. • Histogram Ridges Analysis : suggested in the paper “A fast histogram-clustering approach for multi-level thresholding” by Du-Ming Tsau and Ting-Hsiuing Chen • Important parameter: the distance between the leftmost and rightmost ridge because this distance is related with the intensity range of valid content in the image.

  8. Adaptive Enhancement Algorithm Step 1. Extract Intensity Value in the input image

  9. Step 2. Compute the intensity histogram of the input image

  10. Step 3. Threshold the intensity histogram Against noise and stretch to [0,255] Smoothing with Gaussian smoothing Operator with variance = 2.0 • Find valid ridges and distance • between the ridges (Dr) • this is related with the intensity range of valid content in the image.

  11. Step 4. Filter the histogram obtained in step 2 with a filtering coefficient to eliminate noise or unimportant details

  12. Step 5. Compress the detail region and expend important region by using entropy in darker and lighter side

  13. Step 6. Minimum EER finding process After gamma correction with the parameter obtained in minimum EER F.P

  14. Results Before, Histogram Enhancement After Adaptive Enhancement

  15. Comparison I. Classical histogram equalization (HE) Adaptive histogram enhancement (AE)

  16. Comparison II. Image with very light back ground AE Original image HE

  17. Conclusion and future works • Image enhancement is very important technique for face detection, especially in the images acquired in unconstrained illumination condition • Unsuitable enhancement can increase detection-failure rate • AE algorithm estimate the image quality base on EER and intensity histogram and select best transform  It performs much better than classical HE method

  18. Question ?

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