Facial Parts Recognition Using Lifting Wavelet Filters Learned by Kurtosis-minimization

We propose a method for recognizing facial parts using the lifting wavelet filters learned by kurtosisminimization. This method is based on the following three features of kurtosis: If a random variable has a gaussian distribution, its kurtosis is zero. If the kurtosis is positive, the respective distribution is supergaussian. The value of kurtosis is bounded below. It is known that the histogram of wavelet coefficients for a natural image behaves like a supergaussian distribution. Exploiting these properties, free parameters included in the lifting wavelet filter are learned so that the kurtosis of lifting wavelet coefficients for the target facial part is minimized. Since this minimization problem is an ill-posed problem, it is solved by employing the regularization method. Facial parts recognition is accomplished by extracting facial parts similar to the target facial part. In simulation, a lifting wavelet filter is learned using the narrow eyes of a female, and the learned lifting filter is applied to facial images of 10 females and 10 males, whose expressions are neutral, smile, anger, and scream, to recognize eye part.