Multimodal Biometric Human Recognition for Perceptual Human-Computer Interaction

Richard M. Jiang, Abdul H. Sadka, Danny Crookes

Research output: Contribution to journalArticlepeer-review

34 Citations (Scopus)
2 Downloads (Pure)


In this paper, a novel video-based multimodal biometric verification scheme using the subspace-based low-level feature fusion of face and speech is developed for specific speaker recognition for perceptual human--computer interaction (HCI). In the proposed scheme, human face is tracked and face pose is estimated to weight the detected facelike regions in successive frames, where ill-posed faces and false-positive detections are assigned with lower credit to enhance the accuracy. In the audio modality, mel-frequency cepstral coefficients are extracted for voice-based biometric verification. In the fusion step, features from both modalities are projected into nonlinear Laplacian Eigenmap subspace for multimodal speaker recognition and combined at low level. The proposed approach is tested on the video database of ten human subjects, and the results show that the proposed scheme can attain better accuracy in comparison with the conventional multimodal fusion using latent semantic analysis as well as the single-modality verifications. The experiment on MATLAB shows the potential of the proposed scheme to attain the real-time performance for perceptual HCI applications.
Original languageEnglish
Article number5491122
Pages (from-to)676-681
Number of pages6
JournalIEEE Transactions on System, Man and Cybernetics, Part C
Issue number6
Publication statusPublished - Oct 2010

ASJC Scopus subject areas

  • Computer Science Applications
  • Human-Computer Interaction
  • Information Systems
  • Software
  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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