IEEE Trans Image Process - A novel local pattern descriptor--local vector pattern in high-order derivative space for face recognition.

Tópicos

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Resumo

In this paper, a novel local pattern descriptor generated by the proposed local vector pattern (LVP) in high-order derivative space is presented for use in face recognition. Based on the vector of each pixel constructed by computing the values between the referenced pixel and the adjacent pixels with diverse distances from different directions, the vector representation of the referenced pixel is generated to provide the 1D structure of micropatterns. With the devise of pairwise direction of vector for each pixel, the LVP reduces the feature length via comparative space transform to encode various spatial surrounding relationships between the referenced pixel and its neighborhood pixels. Besides, the concatenation of LVPs is compacted to produce more distinctive features. To effectively extract more detailed discriminative information in a given subregion, the vector of LVP is refined by varying local derivative directions from the n th-order LVP in (n-1) th-order derivative space, which is a much more resilient structure of micropatterns than standard local pattern descriptors. The proposed LVP is compared with the existing local pattern descriptors including local binary pattern (LBP), local derivative pattern (LDP), and local tetra pattern (LTrP) to evaluate the performances from input grayscale face images. In addition, extensive experiments conducting on benchmark face image databases, FERET, CAS-PEAL, CMU-PIE, Extended Yale B, and LFW, demonstrate that the proposed LVP in high-order derivative space indeed performs much better than LBP, LDP, and LTrP in face recognition.

Resumo Limpo

paper novel local pattern descriptor generat propos local vector pattern lvp highord deriv space present use face recognit base vector pixel construct comput valu referenc pixel adjac pixel divers distanc differ direct vector represent referenc pixel generat provid d structur micropattern devis pairwis direct vector pixel lvp reduc featur length via compar space transform encod various spatial surround relationship referenc pixel neighborhood pixel besid concaten lvps compact produc distinct featur effect extract detail discrimin inform given subregion vector lvp refin vari local deriv direct n thorder lvp n thorder deriv space much resili structur micropattern standard local pattern descriptor propos lvp compar exist local pattern descriptor includ local binari pattern lbp local deriv pattern ldp local tetra pattern ltrp evalu perform input grayscal face imag addit extens experi conduct benchmark face imag databas feret caspeal cmupi extend yale b lfw demonstr propos lvp highord deriv space inde perform much better lbp ldp ltrp face recognit

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