Recognizing human gender plays an important role in many human computer interaction (HCI) areas. For example, search engines need an image filter to determine the gender of people in images from the Internet; demographic research can use gender information extracted from images to count the number of men and women entering a shopping mall or movie theater; a “smart building”might use gender for surveillance and control of access to certain areas. Besides these kinds of broad applications, gender recognition itself is an important research topic in both psychology and computer vision.
In psychology studies for HCI, the main focus is about how humans discriminate between males and females and what kind of features are more discriminative. A successful gender classification approach can boost the performance of many other applications including face recognition and smart human-computer interfaces. Despite its importance, it has received relatively little attention in the literature.
We have developed a system for facial gender recognition that is capable to extract from image most informative features using an approach based on genetic algorithms.
The code has been tested with Stanford Medical Student Face Database achievingan excellent recognition rate of 93.60% (200 female images and 200 male images, 90% used for training and 10% used for testing, hence there are 360 training images and 40 test images in total randomly selected and no overlap exists between the training and test images).
Index Terms: Matlab, source, code, gender, recognition, male, female, genetic, algorithm, algorithms, GA.
 | Figure 1. Facial image | ||
A simple and effective source code for Gender Recognition Based on Genetic Algorithms | |||
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