Digital Image Processing
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
Robust Real-Time Face Detection
International Journal of Computer Vision
2D and 3D face recognition: A survey
Pattern Recognition Letters
Biologically Motivated Face Selective Attention Model
Neural Information Processing
Improving AdaBoost Based Face Detection Using Face-Color Preferable Selective Attention
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Audio-visual human recognition using semi-supervised spectral learning and hidden Markov models
Journal of Visual Languages and Computing
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In this paper, we propose a new multiple sensory fused human identification model for providing human augmented cognition. In the proposed model, both facial features and mel-frequency cepstral coefficients (MFCCs) are considered as visual features and auditory features for identifying a human, respectively. As well, an adaboosting model identifies a human using the integrated sensory features of both visual and auditory features. In the proposed model, facial form features are obtained from the principal component analysis (PCA) of a human's face area localized by an Adaboost algorithm in conjunction with a skin color preferable attention model. Moreover, MFCCs are extracted from human speech. Thus, the proposed multiple sensory integration model is aimed to enhance the performance of human identification by considering both visual and auditory complementarily working under partly distorted sensory environments. A human augmented cognition system with the proposed human identification model is implemented as a goggle type, on which it presents information such as unknown people's profile based on human identification. Experimental results show that the proposed model can plausibly conduct human identification in an indoor meeting situation.