Sift Research Paper

Many approaches have been proposed for face recognition.The SIFT has properties to match different images and objects [1]. Section 4 describes a brief introduction to Discrete Wavelet Transform (DWT).

Many approaches have been proposed for face recognition.

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They used Gabor wavelets and multistage model to extract permanent features, and canny edge detection method for transient features. In 2004, Pantic and Rothkrantz [4] proposed a way to recognize facial expressions in front view and profile view using rule based classifier on 25 subjects of face expression with 86% accuracy.

Buciu and Pitas [5] proposed a technique discriminant nonnegative matrix factorization (DNMF) and compared it with local nonnegative matrix factorization (LNMF) algorithm and nonnegative matrix factorization (NMF) method.

Copyright © 2016 Nirvair Neeru and Lakhwinder Kaur.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The experiments demonstrate that the features extracted by LBP are very efficient for recognition of facial expression and also for the images with low resolution.

In 2008, Kharat and Dudul [7] used Discrete Cosine Transform (DCT), Fast Fourier Transform (FFT), and Singular Value Decomposition (SVD) and extracted features for recognition of emotions (Sadness, Happiness, Fear, Surprise, and Neutrality).

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Face recognition systems are mostly used as a mass security measure and user authentication and so forth; the faces can be easily recognized by humans, but automatic recognition of face by machine is a difficult and complex task.

Furthermore, it is not possible that a human being always conveys the same expression of face.