Publication

Robust Person Verification based on Speech and Facial Images

1999
Conference Papers

Abstract

This paper describes a multi-modal person verification system using speech and frontal face images. We consider two different speaker verification algorithms, a text-independent method using a second-order statistical measure and a text-dependent method based on hidden Markov modelling, as well as a face verification technique using a robust form of corellation. Fusion of the different recognition modules is performed by a Support Vector Machine classifier. Experimental results obtained on the audio-visual database XM2VTS for individual modalities and their combinations show that multimodal systems yield better performances than individual modules for all cases.

Official source

https://infoscience.epfl.ch/entities/publication/2b12ffe7-decf-408b-973f-56f7315edf09

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1999
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/2b12ffe7-decf-408b-973f-56f7315edf09

About this result

Related concepts (32)

Facial recognition system

A facial recognition system is a technology potentially capable of matching a human face from a or a video frame against a database of faces. Such a system is typically employed to authenticate users through ID verification services, and works by pinpointing and measuring facial features from a given image. Development began on similar systems in the 1960s, beginning as a form of computer application. Since their inception, facial recognition systems have seen wider uses in recent times on smartphones and in other forms of technology, such as robotics.

Face detection

Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images. Face detection also refers to the psychological process by which humans locate and attend to faces in a visual scene. Face detection can be regarded as a specific case of object-class detection. In object-class detection, the task is to find the locations and sizes of all objects in an image that belong to a given class. Examples include upper torsos, pedestrians, and cars.

Support vector machine

In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories by Vladimir Vapnik with colleagues (Boser et al., 1992, Guyon et al., 1993, Cortes and Vapnik, 1995, Vapnik et al., 1997) SVMs are one of the most robust prediction methods, being based on statistical learning frameworks or VC theory proposed by Vapnik (1982, 1995) and Chervonenkis (1974).

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Robust Person Verification based on Speech and Facial Images

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