  Biometric authentication techniques are more consistent and efficient than
conventional 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 between
Principle Component Analysis (PCA) and Independent Component Analysis (ICA)
which are used for feature extraction on the basis of different Artificial
Neural Network (ANN) such as Back Propagation (BP), Radial Basis Function (RBF)
and Learning Vector Quantization (LVQ). In this paper, we have chosen "TULIPS1
database, (Movellan, 1995)" which is a small audiovisual database of 12
subjects saying the first 4 digits in English for the incorporation of above
methods. The six geometric lip features i.e. height of the outer corners of the
mouth, width of the outer corners of the mouth, height of the inner corners of
the 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 information
are considered for the research work. After the comprehensive analysis and
evaluation a maximum of 91.07% accuracy in speaker recognition is achieved
using PCA and RBF and 87.36% accuracy is achieved using ICA and RBF. Speaker
identification has a wide scope of applications such as access control,
monitoring, transaction authentication, information retrieval, forensics, etc.
