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Biometric authentication techniques are more consistent and efficient thanconventional authentication techniques and can be used in monitoring,transaction authentication, information retrieval, access control, forensics,etc. In this paper, we have presented a detailed comparative analysis betweenPrinciple Component Analysis (PCA) and Independent Component Analysis (ICA)which are used for feature extraction on the basis of different ArtificialNeural Network (ANN) such as Back Propagation (BP), Radial Basis Function (RBF)and Learning Vector Quantization (LVQ). In this paper, we have chosen "TULIPS1database, (Movellan, 1995)" which is a small audiovisual database of 12subjects saying the first 4 digits in English for the incorporation of abovemethods. The six geometric lip features i.e. height of the outer corners of themouth, width of the outer corners of the mouth, height of the inner corners ofthe mouth, width of the inner corners of the mouth, height of the upper lip,and height of the lower lip which extracts the identity relevant informationare considered for the research work. After the comprehensive analysis andevaluation a maximum of 91.07% accuracy in speaker recognition is achievedusing PCA and RBF and 87.36% accuracy is achieved using ICA and RBF. Speakeridentification has a wide scope of applications such as access control,monitoring, transaction authentication, information retrieval, forensics, etc.

Intelligent System for Speaker Identification using Lip features with PCA and ICA