Multibiometric system using fuzzy level set, and genetic and evolutionary feature extraction

• Published in: IET biometrics Vol. 4; no. 3; pp. 151 - 161
• Main Authors: Roy, Kaushik, Shelton, Joseph, O'Connor, Brian, Kamel, Mohamed S
• Format: Journal Article
• Language: English
• Published: Stevenage The Institution of Engineering and Technology 01.09.2015; John Wiley & Sons, Inc
• Subjects: Accuracy; Approximation; Biometrics; blurred iris‐sclera boundary; Cameras; Clustering; Datasets; Evolution; Evolutionary; Evolutionary computation; face features; face recognition; FCMLS method; Feature based; Feature extraction; feature extraction optimisation technique; Feature recognition; Feature selection; fuzzy C‐means clustering with level set method; fuzzy level set; fuzzy set theory; Fuzzy sets; GEFE; genetic algorithms; genetic and evolutionary feature extraction; genetic computation; Genetics; Image processing; image recognition; Image segmentation; iris features; iris localisation scheme; iris recognition; local binary pattern feature extractors; Localization; LS propagation; LS‐based curve evolution approach; multimodal biometric recognition; multimodal face image datasets; multimodal iris image datasets; multimodal system; Neural networks; nonideal iris images; over‐segmentation; pattern clustering; Pattern recognition; single modal biometric system; Spatial data; Wavelet transforms; over-segmentation; fuzzy set theory; face features; local binary pattern feature extractors; FCMLS method; genetic computation; feature extraction; image segmentation; face recognition; nonideal iris images; iris localisation scheme; multimodal system; LS-based curve evolution approach; single modal biometric system; blurred iris-sclera boundary; multimodal biometric recognition; multimodal iris image datasets; fuzzy level set; fuzzy C-means clustering with level set method; genetic algorithms; genetic and evolutionary feature extraction; evolutionary computation; pattern clustering; multimodal face image datasets; iris features; LS propagation; feature extraction optimisation technique; iris recognition; GEFE; image recognition
• ISSN: 2047-4938; 2047-4946; 2047-4946
• DOI: 10.1049/iet-bmt.2014.0064

This study presents a multimodal system that optimises and integrates the iris and face features based on fusion at the score level. The proposed multibiometric system has two novelties as compared with the previous work. First, the authors deploy a fuzzy C-means clustering with level set (FCMLS) method in an effort to localise the non-ideal iris images accurately. The FCMLS method incorporates the spatial information into the level set (LS)-based curve evolution approach and regularises the LS propagation locally. The proposed iris localisation scheme based on FCMLS avoids over-segmentation and performs well against blurred iris/sclera boundary. Second, genetic and evolutionary feature extraction (GEFE) is applied towards multimodal biometric recognition. GEFE uses genetic and evolutionary computation to evolve local binary pattern feature extractors to elicit distinctive features from the iris and facial images. Different weights for each modality are investigated to determine the significance of each modality. By using the FCMLS method to segment an iris image accurately, as well as using GEFE on a multibiometric dataset, the authors note improved performance of identification and verification accuracies over subjects on a unimodal dataset. More specifically, on the multimodal dataset of face and iris images, GEFE had an identification accuracy of 100%.