Paper Title
Person Authentication Using Face And Voice Modalities

the paper deals with face recognition and speaker recognition algorithms and their application for enhanced multimodal biometrics authentication approach to achieve better performance. Biometric technologies refer to identifying individuals based on their distinguishing biological or behavioral traits such as face, speech, fingerprints, retina, iris, etc. The convenience of biometric security systems and their acceptable authentication performance have led to the integration of biometric systems into desktops, laptops, PDAs and mobilephones. A multimodal personal authentication approach that combines information obtained from face and voice modalities is presented in this paper. In this work, a computationally efficient Principal Component Analysis using Eigen faces algorithm is used for face recognition and in voice authentication, pitch frequency and the Mel frequency cepstral coefficients (MFCC) are employed as voice features, and the K-means clustering algorithm is applied to represent the voice signal. The performance of the algorithms is evaluated through computer simulations and is found to be quite effective.