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An Experimental Investigation about the Integration of Facial Dynamics in Video-Based Face Recognition
Hadid, Abdenour (University of Oulu (Finlàndia). Department of Electrical and Information Engineering)
Pietikäinen, Matti (University of Oulu (Finlàndia). Department of Electrical and Information Engineering)

Data: 2005
Resum: Recent psychological and neural studies indicate that when people talk their changing facial expressions and head movements provide a dynamic cue for recognition. Therefore, both fixed facial features and dynamic personal characteristics are used in the human visual system (HVS) to recognize faces. However, most automatic recognition systems use only the static information as it is unclear how the dynamic cue can be integrated and exploited. The few works attempting to combine facial structure and its dynamics do not consider the relative importance of these two cues. They rather combine the two cues in an adhoc manner. But what is the relative importance of these two cues separately? Does combining them enhance systematically the recognition performance? To date, no work has extensively studied these issues. In this article, we investigate these issues by analyzing the effects of incorporating the dynamic information in video-based automatic face recognition. We consider two factors (face sequence length and image quality) and study their effects on the performance of video-based systems that attempt to use a spatio-temporal representation instead of one based on a still image. We experiment with two different databases and consider HMM (the temporal hidden Markov model) and ARMA (the auto-regressive and moving average model) as baseline methods for the spatio-temporal representation and PCA and LDA for the image-based one. The extensive experimental results show that motion information enhances also automatic recognition but not in a systematic way as in the HVS.
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. Creative Commons
Llengua: Anglès.
Document: article ; recerca ; publishedVersion
Matèria: Face Recognition ; Facial Dynamics ; Principal Component Analysis (PCA) ; Hidden Markov Models (HMM) ; Auto-Regressive and Moving Average(ARMA) ; View-Based Recognition ; Reconeixement facial ; Dinàmica facial ; Anàlisi de component principal ; Models Box-Jenkins ; Model (S)ARIMA ; Promig Mòbil Integrat d'Autoregressió ; Reconeixement d'objectes 3D ; Reconocimiento facial ; Dinámica facial ; Análisis de componente principal ; Modelos Box-Jenkins ; Modelo (S) ARIMA ; Promedio Móvil Integrado de Autoregressió ; Reconocimiento de objetos 3D
Publicat a: ELCVIA : Electronic Letters on Computer Vision and Image Analysis, V. 5 n. 1 (2005) p. 1-13, ISSN 1577-5097

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DOI: 10.5565/rev/elcvia.80

13 p, 404.8 KB

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