Gender classification has become an essential task in human computer interaction (HCI). Gender classification is used in immense number of applications like passive surveillance, control in smart buildings (restricting access to certain areas based on gender) and supermarkets, gender advertising, security investigation. So far detection of gender using facial features is done by using the methods like Gabor wavelets, artificial neural networks and support vector machine. In this work, facial distance measure is used as a progenitor to achieve the gender classification. The proposed approach performs gender classification using mathematical operations on the frontal pose face images using Matlab. This work can be further evaluated in future by using different databases with various poses other than the frontal pose.

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Automated Traffic sign board classification system is one of the key technologies of Intelligent Transportation Systems (ITS). Traffic Surveillance System is being more and important with improving urban scale and increasing number of vehicles. This Paper presents an intelligent sign board classification method based on blob analysis in traffic surveillance. Processing is done by three main steps: moving object segmentation, blob analysis, and classifying. A Sign board is modelled as a rectangular patch and classified via blob analysis. By processing the blob of sign boards, the meaningful features are extracted. Tracking moving targets is achieved by comparing the extracted features with training data. After classifying the sign boards the system will intimate to user in the form of alarms, sound waves. The experimental results show that the proposed system can provide real-time and useful information for traffic surveillance.

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