A Joint Learning Approach to Face Detection in Wavelet Compressed Domain

Algorithm 3

The proposed face detection algorithm in the wavelet compressed domain.

(i) Given a test image represented in 𝑁-layered wavelet compressed domain

(ii) Each layered-coefficient plane 𝐿_𝑖 is composed of three sub-bands, 𝐿_𝑖^LH, 𝐿_𝑖^HL and 𝐿_𝑖^HH

(iii) Preprocessing

  (1) Apply the bi-linear interpolation to down-sample each sub-band to 1/1.25, 1/1.5,

    and 1/1.75 scales, respectively, and form three additional wavelet layer sets.

(iv) For each of these four sets of the wavelet-layer representation, run the sliding window face

   detection with the scale 𝑖 initialized to 1

  (1) Apply the AdaBoost face classifier to each sliding window which is constructed from

    the coefficients in the planes from 𝐿_𝑖 to 𝐿_𝑖+2.

  (2) If the classifier determines the region is a face, calculate and save the position and

    size of the corresponding window in the original image space based on the shift,

    downsample, and layer information.

  (3) Repeat the previous two steps with the scale 𝑖 incremented by one until the scale 𝑖>𝑁−2.

(v) Postprocessing

  (1) Eliminate the overlapped face regions based on the scores provided by the AdaBoost classifier.

  (2) Output the detected faces.