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Performance evaluation of incremental training method for face recognition using PCA

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Abstract

Relevance of ‘face recognition’ (FR) in the modern world requirements is presented as a case of human machine interaction. Physical conditions that influence the face recognition process regarding the facial features, illumination changes and viewing angles etc. are discussed. Face recognition process predominantly depends on machine perception i.e. information through an array of pixels with respect to the facial image. Details of eigenface approach through the involvement of contemporary algebraic and statistical analysis are revisited. Methodology involved in the Principal Component Analysis and advantages of exposing the data to incremental training (using PCA) are discussed. A model for the implementation of IPCA over the face databases is proposed to estimate its performance for the face recognition process. Performance of the present model is studied in the domain of Euclidean distance, decay parameter, recognition rate, eigenvalues and overall computational time. Present IPCA model administered over standard ORL, FERET databases along with that over the JNTU face database with large number of face images revealed relative performance. The merit of present IPCA is inferred through enhanced recognition rate and reduced complexity (in the algorithm), intelligent eigenvectors and lesser computational time. The results are presented in the wake of the body of data available with other methods.

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Abbreviations

I i :

Training image of dimension m × n

X i :

Converted training image of dimension N × 1

Ψ:

Mean image

Ψ′:

New mean image

Φ i /Φ:

Difference image

C:

Covariance matrix

μ i :

Eigenvector of C

C′:

New covariance matrix

μ ′ i :

Eigenvector of C′

λ i :

Eigenvalue of C/L

L:

Matrix used to reduced dimension of C/C′

A:

Matrix of difference images

ρ ′ i :

Eigenvector of L

E:

Diagonal matrix of eigenvalues of C

μ:

Matrix of Eigenvectors

Ω:

Weight matrix

Γ:

Testing image

Γ f :

Reconstructed image

w k :

Weight of an image

a i :

Element in A

∈/∈ k :

Euclidean distance

F :

Set contains training images

λ′ i :

Eigenvalue of C′

α:

Decay parameter

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Authors and Affiliations

  1. Department of Computer Science and Engineering, JNTU College of Engineering, Kakinada, 533 003, AP, India

    Ch. Satyanarayana

  2. Department of Physics, JNTU College of Engineering, Kakinada, 533003, AP, India

    D. M. Potukuchi

  3. Department of Electronics and Communication Engineering, JNTU College of Engineering, Hyderabad, AP, India

    L. Pratap Reddy

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  1. Ch. Satyanarayana
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  2. D. M. Potukuchi
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  3. L. Pratap Reddy
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Correspondence to Ch. Satyanarayana.

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Satyanarayana, C., Potukuchi, D.M. & Pratap Reddy, L. Performance evaluation of incremental training method for face recognition using PCA. J Real-Time Image Proc 1, 311–327 (2007). https://doi.org/10.1007/s11554-007-0031-3

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