Feature subset selection is the process of choosing the variables that are important for the classification stage from the original feature space. Feature selection is an important task for almost any pattern recognition problem (Webb, 1999). This procedure is aimed to derive as many discriminative cues as possible whilst removing the redundant and irrelevant information which may degrade the recognition rate. Furthermore, feature selection does not only reduce the cost of recognition by reducing the dimensionality of the feature space, but also offers an improved classification performance through a more stable and compact representation (Jain 1982). It is practically infeasible to run an exhaustive search for all the  possible combinations of features in order to obtain the optimal subset for recognition due to the high dimensionality of the feature space.  For this reason, it is recommended to use a feature selection algorithm as  the Adaptive Sequential Forward Floating Selection (ASFFS)  search algorithm (Pudil 1994).

The feature selection procedure fundamentally relies on an evaluation function that determines the usefulness of each feature in order to derive the ideal subset of features for the classification phase. For every feature or set of features generated by the feature selection algorithm, an evaluation criterion is called to measure the discriminative ability of the set of features to distinguish different subjects (Dash 1997). A number of methods (Mowbray, 2003) rely mainly on statistical metric measures which are based on the scatter or distribution of the training samples in the feature space such as the Bhattacharyya metric. These methods aim to find the features which minimize the overlap between the different classes as well as the inner-class scatter.

After playing and making a lot of noise! There comes a judgment day for her! Immunization which made her cry for about half an hour, then fell asleep! Then back to noise and trouble making again!.

And another photo as she is trying to stand up !

Since the adaptive background subtraction lacks capability to remove shadows, it is recommended using the approach described by Cucchiara (2003) to evaluate whether a foreground pixel corresponds to cast shadow based on the Hue Saturation Value (HSV) colour information. The chromaticity and luminosity of the foreground pixels are separated using the HSV colour space which is proved to match the human perception of colour more closely than the RGB model (Herodotou, 1998). The method proposed by Cucchiara et al assumes that shadows reduce surface brightness and saturation while maintaining chromaticity properties in the HSV colour space.

The Harris corner detector is a popular interest point detector due to its invariance to rotation, scale as well as illumination variation and robustness to image noise. The Harris corner detector is based on the local auto-correlation function of a signal; where the local auto-correlation function measures the local changes of the signal with patches shifted by a small amount in different directions.