With the rising demands of public safety systems,
face recognition has gained extra notice for the researchers in
recent times. Real-world face recognition systems require
cautious balancing of two important concerns: execution Time,
recognition rate. The most important methods for feature
extraction and classification are Dimension Reduction-Discrete
Cosine Transform (DR-DCT), Dimension Reduction-Discrete
Fourier Transform (DR-DFT) and Dimension ReductionDifference of Gaussian (DoG) along with the Extreme
Learning Machine (ELM) classifiers. The feature vector of the
proposed algorithm is reduced by means of subspace method
Principal Component Analysis (PCA). The execution time of
the proposed DR-DFT, DR-DoG and DR-DCT algorithms
along with ELM classifier is less when compared to the
traditional methods such as Hough transform, Radon
Transform, Discrete Fourier Transform (DFT) and Discrete
Cosine Transform (DCT) for ORL face database. Similarly,
Hybrid methods are proposed by combining DR-DCT &DRDFT (Hybrid Method 1), DR-DCT & DR-DoG (Hybrid
Method 2) and DR-DFT & DR-DoG (Hybrid Method3) along
with ELM classifier. Hybrid Method 1 attains 98.50%
recognition rate with the feaure size of 60×1 for ORL dataset.
It achieves optimal execution time of 0.020 sec.
Keywords: DR-DCT Dimension Reduction-Discrete
Fourier Transform (DR-DFT), Dimension ReductionDifference of Gaussian (DoG), Extreme Learning Machine
(ELM), Nearest Neighbour (NN) and Principal Component
Analysis (PCA).