This paper addresses face recognition algorithm by means of synthetic discriminant functions in reduced dimension space on appearance-based approach. Being apart from ideal conditions, where standard illumination, expression and frontal view are the normal case, we intend to process available information on the classic manner, providing useful and promising results. Half-tone facial image is 3D intensity shape, which we represent as being composed of a set of 2D binary images. Normalized gray image is sliced on the layers which are further converted to binary maps. 2D truncated Walsh-Hadamard Transform is then applied to these maps leading to significant reduction of dimensionality and producing translation-invariant feature vectors. These vectors are used to construct synthetic discriminant function (SDF), which is considered to be the facial image class descriptor and serves for face identification.
This paper presents appearance-based face identification algorithm by means of synthetic linear filters. The objective of our research is to construct facial descriptor in the form of linear filter, which should produce high and low outputs for intra- and inter-class recognition problem correspondingly. This filter can be synthesized from 2,5D sparse mesh derived from a given set of images of a person. As ever the filter is created it is then used as facial descriptor, i.e. serves as personal ID for face identification.
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