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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.

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An improvement of J.C.S. Jacques, C.R. Jung, S.R. Musse background subtraction and shadow detection in grayscale video sequences method [1] is proposed. Described improvements reduce amount of false positive results of background subtraction (especially in light and monotonous areas) and its sizes, improve background model quality, provide better outcome results.

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