Person identification using face and speech
In this thesis, we present a multimodal biometric system using face and speech features. Multimodal biometrics system uses two or more intrinsic physical or behaviour traits to provide better recognition rate than unimodal biometric systems. Face recognition is built using principal component analysis (PCA) and the Gabor filters. In Face recognition, PCA is applied to Gabor filter bank response of the face images. Speaker recognition is built using amplitude modulation - frequency modulation (AM-FM) features. AM-FM features are weighted-instantaneous frequency of the analytical signal. Finally, weighted sum of score of face and speaker recognition system is used for person identification. Performance of our system is evaluated by using ORL database for face images and ELSDSR database for speech. Experimental results show better recognition rate for the multimodal sytem when compared to unimodal system
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Madhavi, Maulik C. (Dhirubhai Ambani Institute of Information and Communication Technology, 2011)In this thesis, design of person recognition system based on person's hum is presented. As hum is nasalized sound and LP (Linear Predication) model does not characterize nasal sounds sufficiently, our approach in this work ...
Patel, Chirag R. (Dhirubhai Ambani Institute of Information and Communication Technology, 2012)In this thesis, design of person recognition system from their hum is discussed. The emphasis is given to the inter-session variability of the recognition system. Standard database is not available for the inter-session ...
Goswami, Parth A. (Dhirubhai Ambani Institute of Information and Communication Technology, 2011)This thesis deals with the Automatic Speaker Recognition (ASR) system over narrowband Voice over Internet Protocol (VoIP) networks. There are several artifacts of VoIP network such as speech codec, packet loss and packet ...