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Gender Recognition System for Windows

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Gender Recognition System Analysis

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Gender Recognition System is a free Windows application designed to analyze and identify gender based on input data. This educational software utilizes advanced algorithms to assess various attributes, providing users with insights into gender recognition technology. The program is particularly useful for researchers, developers, and educators interested in understanding the complexities of gender classification in data analysis.

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The interface is user-friendly, allowing for easy navigation and accessibility. Users can input various types of data, and the system processes this information to deliver results efficiently. With its focus on educational purposes, Gender Recognition System serves as a valuable reference tool for those studying artificial intelligence and machine learning applications in gender recognition.

Icon of program: Gender Recognition System

Gender Recognition System for Windows

Download now

Used Gender Recognition System for Windows?

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Full Specifications

GENERAL
Release
Latest update
Version
2.0
OPERATING SYSTEMS
Platform
Windows
Operating System
  • Windows 2000
  • Windows 2003
  • Windows 10
  • Windows Server
  • Windows 8
  • Windows 98
  • Windows XP
  • Windows ME
  • Windows NT
  • Windows Vista
  • Windows 7
Additional Requirements
Matlab
POPULARITY
Total Downloads
679
Downloads Last Week
0
Report Software

Last Updated

Developer’s Description

By Luigi Rosa
Provide gender recognition system for Matlab.
Human face contains a variety of information for adaptive social interactions amongst people. In fact, individuals are able to process a face in a variety of ways to categorize it by its identity, along with a number of other demographic characteristics, such as gender, ethnicity, and age. In particular, recognizing human gender is important since people respond differently according to gender. In addition, a successful gender classification approach can boost the performance of many other applications, including person recognition and smart human-computer interfaces. We have developed an algorithm for gender recognition based on AdaBoost algorithm. Boosting has been proposed to improve the accuracy of any given learning algorithm. In Boosting one generally creates a classifier with accuracy on the training set greater than an average performance, and then adds new component classifiers to form an ensemble whose joint decision rule has arbitrarily high accuracy on the training set. In such a case, we say that the classification performance has been "boosted". In overview, the technique train successive component classifiers with a subset of the entire training data that is "most informative" given the current set of component classifiers. AdaBoost (Adaptive Boosting) is a typical instance of Boosting learning. In AdaBoost, each training pattern is assigned a weight that determines its probability of being selected for some individual component classifier. Generally, one initializes the weights across the training set to be uniform. In the learning process, if a training pattern has been accurately classified, then its chance of being used again in a subsequent component classifier is decreased; conversely, if the pattern is not accurately classified, then its chance of being used again is increased.The code has been tested with Stanford Medical Student Face Database achieving an excellent recognition rate of 89.61% (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, identification, adaboost, male, female.
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AI Assisted Content Disclosure

Content created and reviewed by Softonic with information obtained from Luigi Rosa, using AI.

CNET's editorial team was not involved in the creation of this content. Opinions, analysis and reviews were not provided by CNET.