Face Recognition Using LDA-Based Algorithms  

Oleh:

Lu, Juwei ; Plataniotis, K. N. ; Venetsanopoulos, A. N.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 14 no. 1 (Jan. 2003), page 195-200.
Topik: face recognition; face recognition; LDA - based algorithms

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.8 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

Low - dimensional feature representation with enhanced discriminatory power is of paramount importance to face recognition (FR) systems. Most of traditional linear discriminant analysis (LDA) - based methods suffer from the disadvantage that their optimality criteria are not directly related to the classification ability of the obtained feature representation. Moreover, their classification accuracy is affected by the "small sample size" (SSS) problem which is often encountered in FR tasks. In this paper, we propose a new algorithm that deals with both of the shortcomings in an efficient and cost effective manner. The proposed method is compared, in terms of classification accuracy, to other commonly used FR methods on two face databases. Results indicate that the performance of the proposed method is overall superior to those of traditional FR approaches, such as the eigenfaces, fisherfaces, and D - LDA methods.

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