Online Person Identification based on Multitask Learning

In the digital world, everything is digitized and data are generated consecutively over the times. To deal with this situation, incremental learning plays an important role. One of the important applications that needs an incremental learning is person identification. On the other hand, password and code are no longer the only way to prevent the unauthorized person to access the information and it tends to be forgotten.  Therefore, biometric characteristics system is introduced to solve the problems. However, recognition based on single biometric may not be effective, thus, multitask learning is needed. To solve the problems, incremental learning is applied for person identification based on multitask learning. Considering that the complete data is not possible to be collected at one time, online learning is adopted to update the system accordingly. Linear Discriminant Analysis (LDA) is used to create a feature space while Incremental LDA (ILDA) is adopted to update LDA. Through multitask learning, not only human faces are trained, but fingerprint images are trained in order to improve the performance. The performance of the system is evaluated by using 50 datasets which includes both male and female datasets. Experimental results demonstrate that the learning time of ILDA is faster than LDA. Apart from that, the learning accuracies are evaluated by using K-Nearest Neighbor (KNN) and achieve more than 80% for most of the simulation results. In the future, the system is suggested to be improved by using better sensor for all the biometrics. Other than that, incremental feature extraction is improved to deal with some other online learning problems

Online Person Identification based on Multitask Learning

In the digital world, everything is digitized and data are generated consecutively over the times. To deal with this situation, incremental learning plays an important role. One of the important applications that needs an incremental learning is person identification. On the other hand, password and code are no longer the only way to prevent the unauthorized person to access the information and it tends to be forgotten. Therefore, biometric characteristics system is introduced to solve the problems. However, recognition based on single biometric may not be effective, thus, multitask learning is needed. To solve the problems, incremental learning is applied for person identification based on multitask learning. Considering that the complete data is not possible to be collected at one time, online learning is adopted to update the system accordingly. Linear Discriminant Analysis (LDA) is used to create a feature space while Incremental LDA (ILDA) is adopted to update LDA. Through multitask learning, not only human faces are trained, but fingerprint images are trained in order to improve the performance. The performance of the system is evaluated by using 50 datasets which includes both male and female datasets. Experimental results demonstrate that the learning time of ILDA is faster than LDA. Apart from that, the learning accuracies are evaluated by using K-Nearest Neighbor (KNN) and achieve more than 80% for most of the simulation results. In the future, the system is suggested to be improved by using better sensor for all the biometrics. Other than that, incremental feature extraction is improved to deal with some other online learning problems

Multimodal Biometrics Enhancement Recognition System based on Fusion of Fingerprint and PalmPrint: A Review

This article is an overview of a current multimodal biometrics research based on fingerprint and palm-print. It explains the pervious study for each modal separately and its fusion technique with another biometric modal. The basic biometric system consists of four stages: firstly, the sensor which is used for enrolmen

Multimodal biometrics score level fusion using non-confidence information

Multimodal biometrics refers to automatic authentication methods that depend on multiple modalities of measurable physical characteristics. It alleviates most of the restrictions of single biometrics. To combine the multimodal biometrics scores, three different categories of fusion approaches including rule based, classification based and density based approaches are available. When choosing an approach, one has to consider not only the fusion performance, but also system requirements and other circumstances. In the context of verification, classification errors arise from samples in the overlapping region (or non- confidence region) between genuine users and impostors. In score space, a further separation of the samples outside the non-confidence region does not result in further verification improvements. Therefore, information contained in the non-confidence region might be useful for improving the fusion process. Up to this point, no attempts are reported in the literature that tries to enhance the fusion process using this additional information. In this work, the use of this information is explored in rule based and density based approaches mentioned above