Whenever people log onto computers, access an ATM, pass through airport security, use credit cards, or enter high-security areas, they need to verify their identities. Thus, there is tremendous interest in improved methods for reliable and secure identification of people. Gender classification based on iris images is currently one of the most challenging problems in image analysis research. In a biometric recognition framework, gender classification can help by requiring a search of only half of the subjects in the database.
One active area of soft biometric research involves classifying the gender of the person from the biometric sample. Most work done on gender classification has involved the analysis of face images and uses Local Binary Patterns (LBP) to increase the accuracy of the identification task. Various types of classifiers have been used in gender classification after feature extraction and selection. Gender recognition is a fundamental task for human beings, as many social functions critically depend on the correct gender perception. Automatic gender classification has many important applications, for example, intelligent user interface, visual surveillance, collecting demographic statistics for marketing, etc. Human faces provides important visual information for gender classification.
Feature selection on NIR images: