Analysis of Face Recognition Techniques with Grayscale and Color Preprocessing

The above histogram represents the image of faces stored in database captured in different lighting conditions by using local binary pattern histogram4. Figure

This dataset covers one year to enable it to capture all of the most critical acquisition conditions that could have affected the quality of the content-based image

As shown in figure 5 the face is detected in a captured image in real time & in static conditions by using proposed PLMSE face detection method, in the

 The quality of the image in the database ORL is better than the database FEI.  Database FEI is more difficult due to variations in the details of the face and

[53] Chao-Kuei Hsieh, Shang-Hong Lai and Yung-Chang Chen “An Optical Flow-Based Approach to Robust Face Recognition Under Expression Variations” :IEEE transactions on

PCA and wavelet transform methods are used to extract features from face image using and identify the image of the face using SVMs classifier as well as the neural network are used

In this section, three popular appearance-based statistical methods, namely Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Linear

The experimental results shown that Gradient faces normalization techniques achieved better recognition rate compared to different normalization techniques as