Many approaches have been proposed for face recognition.The SIFT has properties to match different images and objects . Section 4 describes a brief introduction to Discrete Wavelet Transform (DWT).
Many approaches have been proposed for face recognition.Tags: Thesis In Tourism MarketingDental Business PlanGreat Application Essays For Business School ScribdWilliam Lobdell EssayJournal For Publishing Research PaperAngels In America EssaysA Good Thesis Statement IsBator An Essay On The International Trade In Art
They used Gabor wavelets and multistage model to extract permanent features, and canny edge detection method for transient features. In 2004, Pantic and Rothkrantz  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  proposed a technique discriminant nonnegative matrix factorization (DNMF) and compared it with local nonnegative matrix factorization (LNMF) algorithm and nonnegative matrix factorization (NMF) method.
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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  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).
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.