Multi-modal gender classification using support vector machines (SVMs)

• US 20030110038A1
• Filed: 10/16/2002
• Published: 06/12/2003
• Est. Priority Date: 10/16/2001
• Status: Abandoned Application

• Current Assignee: Penn State Research Foundation (Pennsylvania State University)
• Original Assignee: Penn State Research Foundation (Pennsylvania State University)
• Inventors: Yeasin, Mohammed, Walavalkar, Leena A., Sharma, Rajeev
• Application Number:
  US10/271,911
• Publication Number:
  US 20030110038A1
• Time in Patent Office: Days
• US Class Current: 704/270
• CPC Class Codes: G06V 40/16 Human faces, e.g. facial parts, sketches or expressions
• CPC Class Codes: G10L 17/10 Multimodal systems, i.e. based on the integration of multiple recognition engines or fusion of expert systems

Abstract

A multi-modal system for determining the gender of a person using support vector machines (SVMs). Gender classification is first performed on visual (thumbnail frontal face) and audio (feature extracted from speech) data using support vector machines (SVMs). The decisions obtained from individual SVM-based gender classifiers are used as input to train a final classifier to decide the gender of an individual.

Claims

1. A computer software system for multi-modal human gender classification, comprising:

a first-mode classifier classifying first-mode data pertaining to male and female subjects according to gender and rendering a first-mode gender-decision for each male and female subject;

a second-mode classifier classifying second-mode data pertaining to male and female subjects according to gender and rendering a second-mode gender-decision for each male and female subject; and

a fusion classifier integrating the individual gender decisions obtained from said first-mode classifier and said second-mode classifier and outputting a joint gender decision for each of said male and female subjects.

2. A computer software system as set forth in claim 1, wherein said first mode classifier is a vision-based classifier;

and

wherein said second mode classifier is a speech-based classifier.

3. A computer software system as set forth in claim 2, wherein said speech-based classifier comprises a support vector machine.

4. A computer software system as set forth in claim 2, wherein said first-mode classifier, second-mode classifier, and fusion classifier each comprise a support vector machine.

5. A computer software system for multi-modal human gender classification, comprising:

means for storing a database comprising a plurality of male and female facial images to be classified according to gender;

means for classifying the male and female facial images according to gender;

means for storing a database comprising a plurality of male and female utterances to be classified according to gender;

means for classifying the male and female utterances according to gender;

means for integrating the individual gender decisions obtained from the vision and speech based classification means to obtain a joint gender decision, said multi-modal gender classification having a higher performance measurement than the vision or speech based means individually.

6. A multi-modal method for human gender classification, comprising the following steps, executed by a computer:

generating a database comprising a plurality of male and female facial images to be classified;

extracting a thumbnail face image from said database;

training a support vector machine classifier to differentiate between a male and a female facial image, comprising determining an appropriate polynomial kernel and the bounds on Lagrange multiplier;

generating a database comprising a plurality of male and female utterances to be classified;

extracting a Cepstrum feature from said database;

training a support vector machine classifier to differentiate between a male and a female utterance, comprising determining an appropriate Radial Basis Function and the bounds on Lagrange multiplier;

integrating the individual gender decisions obtained from the speech and vision based support vector machine classifiers, using a semantic fusion method, to obtain a joint gender decision, said multi-modal gender classification having a higher performance measurement that the speech or vision based modules individually.

7. The method of claim 6 wherein the performance of the support vector machine classifier is further augmented, comprising the steps of:

testing the support vector machine classifier by employing a plurality of refinement male and female facial images to be classified by the support vector machine classifier according to gender; and

using the refinement facial images for which gender was improperly detected to augment and reinforce the support vector machine learning process.

8. The method of claim 7 wherein the performance of the support vector machine classifier is further augmented, comprising the steps of:

testing the support vector machine classifier by employing a plurality of refinement male and female utterances to be classified by the support vector machine classifier according to gender; and

using the refinement utterances for which gender was improperly detected to augment and reinforce the support vector machine learning process.