{'fraction_non_alphanumeric': 0.02130681818181818, 'fraction_numerical': 0.008522727272727272, 'mean_word_length': 5.077586206896552, '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}}
Minutiae Based Feature Level Fusion for Multimodal Biometrics

Multimodal biometrics aims to authenticate an individual with the help of one or more biometric traits. This paper has focused on integrating the minutiae features of retina and fingerprint with feature level fusion. Time optimization is done using Clustering. Extracted Minutiae features of both fingerprint and retina are clustered using K-means Clustering and they are Indexed. Clustered and Indexed data stored in a database is compared with the query future and nearest neighbourhood clusters are identified. Decision is made based on the similarity measure. The proposed system provides an authentication with GAR = 99.96%, FAR = 0.4%.