Fast Object Detection using MLP and FFT

We propose a new technique for a faster computation of the activities of the hidden layer units. This has been demonstrated on face detection examples

Robust Person Verification based on Speech and Facial Images

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

Fusion of Face and Speech Data for Person Identity Verification

Multi-modal person identity authentication is gaining more and more attention in the biometrics area. Combining different modalities increases the performance and robustness of identity authentication systems. The authentication problem is a binary classification problem. The fusion of different modalities can be therefore performed by binary classifiers. We propose to evaluate different binary classification schemes (SVM, MLP, C4.5, Fisher's linear discriminant, Bayesian classifier) on a large database (295 subjects) containing audio and video data. The identity authentication is based on two modalities: face and speec

Fast Face Detection using MLP and FFT

We propose a new technique for a faster computation of the activities of the hidden layer units. This has been demonstrated on face detection examples

Audio-Visual Person Verification

In this paper we investigate benefits of classifier combination for a multimodal system for personal identity verification. The system uses frontal face images and speech. We show that a sophisticated fusion strategy enables the system to outperform its facial and vocal modules when taken separately. We show that both trained linear weighted schemes and fusion by Support Vector Machine classifier leads to a significant reduction of total error rates. The complete system is tested on data from a publicly available audio-visual database according to a published protocol

Fast Object Detection using MLP and FFT

submitted for publication Abstract. We propose a new technique that speeds up signi cantly the time needed by a trained network (MLP in our case) to detect a face in a large image. We reformulate neural activities in the hidden layer of the MLP in terms of lter convolution enabling the use of Fourier transform for an e cient computation of the neural activities. A formal proof and a complexity analysis are presented. Finally, some examples illustrate the approach. 2 IDIAP{RR 97-11

Multi-Modal Data Fusion for Person Authentication using SVM

In the context of multi-modal person authentication, a set of experts (face recognizer, speaker recognizer, etc. ) give their opinion about the identity of an individual. The opinions of the experts can be combined to form a final decision (rejecting or accepting the claim). We show that the final decision is a binary classification problem and propose to solve it by a Support Vector Machine (SVM). We compare our approach with other proposed methods for an identical verification task and show that it leads to considerably higher performance

Multi-Modal Data Fusion for Person Authentification Using SVM

In the context of multi-modal person authentication, a set of experts (face recognizer, speaker recognizer, etc. ) give their opinion about the identity of an individual. The opinions of the experts can be combined to form a final decision (rejecting or accepting the claim). We show that the final decision is a binary classification problem and propose to solve it by a Support Vector Machine (SVM). We compare our approach with other proposed methods for an identical verification task and show that it leads to considerably higher performance