Face Detection/Gender & Race Classification
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Automatic face detection is a critical component in the new domain of computer human observation and computer human interaction (HCI). There are many examples including: user-interfaces that can detect the presence and number of users; teleconference systems can automatically devote additional bandwidth to participant's faces; video security systems can record facial images of individuals after unauthorized entry; and indexing of image and video content can benefit from meta-data from face detection and classification. This is an extension of our previous work on fast face detection. Our new results include a system that can locate facial features such as the eyes, detect rotated and profile faces and determine the gender (male/female) and race (asian / non-asian) of the face.
Background & Objective: Automatic face detection is a critical component in the new domain of computer human observation and computer human interaction (HCI). There are many examples including: user-interfaces that can detect the presence and number of users; teleconference systems can automatically devote additional bandwidth to participant's faces; video security systems can record facial images of individuals after unauthorized entry; and indexing of image and video content can benefit from meta-data from face detection and classification. This is an extension of our previous work on fast face detection. Our new results include a system that can locate facial features such as the eyes, detect rotated and profile faces and determine the gender (male/female) and race (asian / non-asian) of the face.
Technical Discussion: There are three main contributions of our face detection framework. First: a new image representation called an Integral Image that allows for very fast feature evaluation. Second: a method for constructing a classifier by selecting a small number of important features using AdaBoost. In order to ensure fast classification, the  learning process must exclude a large majority of the available features, and focus on a small set of critical features. Third: a method for combining successively more complex classifiers in a cascade structure which dramatically increases the speed of the detector by focusing attention on promising regions of the image. The notion behind focus of attention approaches is that it is often possible to rapidly determine where in an image an object might occur. More complex processing is reserved only for these promising regions.
Contacts:
Michael Jones
| Technical Reports: | |
| Face Recognition Using Boosted Local Features | |
| A Unified Learning Framework for Real Time Face Detection & Recognition | |
Technology Area: Computer Vision
Modification Date: January 23, 2007