Non-adjacent graph structure sparse face recognizing method

Abstract

The invention provides a non-adjacent graph structure sparse face recognizing method. The method comprises the steps of enabling the non-adjacent graph structure to be sparse; searching by blocks or combination; measuring the structure sparseness; performing structure sparse reconstruction. According to the method, the system performances are improved through non-adjacent graph structure sparseness based on an SRC model; the blocking of the non-adjacent graph structure is dynamically performed by an overlapping manner and cannot be predicated; the components can be adjacent or non-adjacent; all possible combinations can be searched by the combination method to gain adjacent or non-adjacent blockings, so as to achieve the non-adjacent graph structure sparseness; to avoid combination explosion in search, the method of searching by blocks or combination is provided for limiting the search space as well as generating computer acceptable basis subset space; the non-adjacent graph structure sparse reconstruction is carried out by the structure greedy algorithm; when in iterating of the algorithm, the base blocks are selected according to the contribution degree of the base blocks; the non-adjacent graph structure sparseness is measured according to the coding complexity. With the adoption of the method, the face recognizing rate can be obviously increased.

Description

technical field [0001] The invention relates to a non-adjacent graph structure sparse face recognition method, which belongs to the technical field of sparse representation face recognition. Background technique [0002] Compressed sensing (Compressed sensing, CS) aims at signals with sparsity or sparsity in a specific domain, by implementing random sampling far below the Nyquist sampling rate, using the sparsity of the signal and the relationship between the measurement matrix and the measurement basis The incoherence between them can accurately reconstruct the original signal with high probability. Driven by compressive sensing theory, sparse coding and sparse representation technologies have developed rapidly in recent years. The idea of ​​sparse representation is to assume that the observed data y∈R n Can be expressed as a data dictionary D∈R n×p sparse linear combination of , namely: y=Dα, where α∈R p is the representation coefficient of y under the dictionary D. T...

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Problems solved by technology

However, the data dictionary of the SRC model does not have such continuit

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