{'fraction_non_alphanumeric': 0.0291583830351226, 'fraction_numerical': 0.007952286282306162, 'mean_word_length': 5.265560165975104, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Expert System for Speaker Identification Using Lip Features with PCA

Biometric authentication techniques such as lips, face, and eyes are more reliable and efficient than conventional authentication techniques such as password authentication, token, cards, personal identification number, etc. In this research paper, the emphasis has been laid on the speaker identification based on lip features. In this study, we have presented a detailed comparative analysis for speaker identification by using lip features, Principal Component Analysis (PCA), and neural network classifiers. PCA has been used for feature extraction from the six geometric lip features which are 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. These features are then used for training of the network by using different neural network classifiers such as Back Propagation (BP), Radial Basis Function (RBF) and Learning Vector Quantization (LVQ). These approaches are incorporated on "TULIPS1 database, (Movellan, 1995)" which is a small audiovisual database of 12 subjects saying the first 4 digits in English. After the detailed analysis and evaluation a maximum of 91.07% accuracy in speaker recognition is obtained using PCA and RBF. Speaker identification has a wide range of applications such as Audio Processing, Medical data, Finance, Array processing, etc.