In this paper, we describe a novel and interesting approach for extracting the image features. The features we propose are efficient and robust; the feature vectors of relatively small dimensions are sufficient for successful recognition. We call them the energy-transfer features. In contrast, the classical features (e.g. HOG, Haar features) that are combined with the trainable classifiers (e.g. a support vector machine, neural network) require large training sets due to their high dimensionality. The large training sets are difficult to acquire in many cases. In addition to that, the large training sets slow down the training phase. Moreover, the high dimension of feature vector also slows down the detection phase and the methods for the reduction of feature vector must be used. These shortcomings became the motivation for creating the features that are able to describe the object of interest with a relatively small number of numerical values without the use of methods for the reduction of feature vector. In this paper, we demonstrate the properties of our features in the task of face detection.
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