Rank level fusion
221591701
Score and Rank Level Fusion Algorithms for Social Behavioral Biometrics
The goal of a biometric system is to recognize individuals based on their unique physiological or behavioral traits. Online Social Networking (OSN) platforms have become an integral part of the daily life of individuals, where they leave a recognizable trail of behavioral information. Social Behavioral Biometric (SBB) being an emerging trend, focuses on such trails to distinguish between individuals. This research investigates the impact of users' writing profiles on OSN to conclude whether such profiles contribute to SBB. The distinctiveness of the SBB features that are extracted from the social behavioral data of Twitter is studied. A person identification system that relies on users' writing profiles, reply, retweet, shared weblink, trendy topic networks and temporal profiles is proposed. Score and rank level weighted fusion algorithm performance is compared on a social interaction database of 241 Twitter users. The experimental results establish that the users' writing profiles have the highest impact over other social biometric features and that score level fusion algorithms perform better than rank level fusion on SBB. The proposed system has achieved recognition rate of 99.45% at rank 1 after cross validation using genetic algorithm based score level fusion algorithm. The system outperformed all prior researches on SBB in terms of identification accuracy.
IEEE Access
Sanjida Nasreen Tumpa Marina L Gavrilova
164321855
Score Level and Rank Level Fusion for Kinect Based Multi Modal Biometric System
Abstract Computational intelligence firmly made its way into the areas of consumer applications, banking, education, social networks, and security. Among all the applications, biometric systems play a significant role in ensuring an uncompromised and secure access to resources and facilities. This article presents a first multimodal biometric system that combines KINECT gait modality with KINECT face modality utilizing the rank level and the score level fusion. For the KINECT gait modality, a new approach is proposed based on the skeletal information processing. The gait cycle is calculated using three consecutive local minima computed for the distance between left and right ankles. The feature distance vectors are calculated for each person's gait cycle, which allows extracting the biometric features such as the mean and the variance of the feature distance vector. For Kinect face recognition, a novel method based on HOG features has been developed. Then, K nearest neighbors feature matching algorithm is applied as feature classification for both gait and face biometrics. Two fusion algorithms are implemented. The combination of Borda count and logistic regression approaches are used in the rank level fusion. The weighted sum method is used for score level fusion. The recognition accuracy obtained for multi modal biometric recognition system tested on KINECT Gait and KINECT Eurocom Face datasets is 93.33% for Borda count rank level fusion, 96.67% for logistic regression rank level fusion and 96.6% for score level fusion.
J. Artif. Intell. Soft Comput. Res.
Md Wasiur Rahman Fatema Tuz Zohra Marina L Gavrilova
54467938
Confidence factor weighted Gaussian function induced parallel fuzzy rank level fusion for inference and its application to face recognition
Abstract This paper proposes a novel approach for inference using fuzzy rank level fusion and explores it application to face recognition using multiple biometric representations. Multiple representations of single biometric (trait) aim to increase the reliability or acceptance of a biometric system, as it exploits the underlying essential characteristics provided by different sensors. In this paper, we propose a new scheme for generating fuzzy ranks induced by a Gaussian function based on the confidence of a classifier. In contrast to the conventional ranking, this fuzzy ranking reflects some associations among the outputs (confidence factors) of a classifier. These fuzzy ranks, yielded by multiple representations of a face image, are fused weighted by the corresponding confidence factors of the classifier to generate the final ranks while recognizing a face. In many real world applications, where multiple traits of a person are unavailable, the proposed method is highly effective. However, it can easily be extended to multimodal biometric systems utilizing multiple classifiers. The experimental results using different feature vectors of a face image employing different classifiers show that the proposed method can significantly improve recognition accuracy as compared to those from individual feature vectors and as well as some commonly used rank level fusion methods.
Inf. Fusion
Jamuna Kanta Sing Aniruddha Dey Manas Ghosh
211057857
Confidence Belief Function Weighted Parallel Rank level Fusion for Face recognition
In this paper, proficient feature extraction techniques using efficient neural networks (NN) with evidence theory for face recognition are presented. This approach is established to reduce the computation periods required by these NN. Evidence theory based single or multi biometric fusion methods have established promising performance, but they cannot handle the uncertainty appropriately, suggesting that further improvement of the performance of single biometric authentication systems. Conventional ranking is upgraded, using some associations among the outputs (belief confidence factors) of a classifier. Then, the final result is achieved by fusing results from the combined classifier output (belief confidence factors) with evidence theory. The face database usually severely affected by various degradations such as, illumination, noise and pose variations etc. which affects the overall recognition accuracy. The outcome establishes that the proposed rank level fusion method attains superior recognition accuracy than other feature extraction and other related rank level fusion approaches.
2019 3rd International Conference on Electronics, Materials Engineering Nano Technology (IEMENTech)
Aniruddha Dey Sourav Kumar Dey Alok Kumar Roy Manas Ghoslr Satadal Chakraborty Debaditya Kundu
219043165
Rank Level Fusion of Multimodal Biometrics Based on Cross Entropy Monte Carlo Method
In unimodal biometric systems, there are several limitations like, non universality, noisy data and other security risks. To overcome these, multimodal biometric systems are increasingly adopted. Multimodal biometric systems fuse information from multiple biometric traits. Rank level fusion is one of the approaches of information fusion for multimodal biometrics. In this paper, rank level fusion is considered as an optimization problem. Its aim is to minimize the distances between an aggregated rank list and each input rank list from individual biometric trait. A solution of this optimization problem has been proposed using cross entropy (CE) Monte Carlo method. The proposed CE method uses two distance measures namely, Spearman footrule and Kendall's tau distances. Superiority of the proposed CE method based on above two distance measures over several existing rank level and score level fusion schemes is achieved on two different datasets.
Shadab Ahmad Rajarshi Pal Avatharam Ganivada
201620665
Hybrid Score and Rank Level Fusion for Person Identification using Face and ECG Data
Uni modal identification systems are vulnerable to errors in sensor data collection and are therefore more likely to misidentify subjects. For instance, relying on data solely from an RGB face camera can cause problems in poorly lit environments or if subjects do not face the camera. Other identification methods such as electrocardiograms (ECG) have issues with improper lead connections to the skin. Errors in identification are minimized through the fusion of information gathered from both of these models. This paper proposes a methodology for combining the identification results of face and ECG data using Part A of the BioVid Heat Pain Database containing synchronized RGB video and ECG data on 87 subjects. Using 10 fold cross validation, face identification was 98.8% accurate, while the ECG identification was 96.1% accurate. By using a fusion approach the identification accuracy improved to 99.8% Our proposed methodology allows for identification accuracies to be significantly improved by using disparate face and ECG models that have non overlapping modalities.
2019 Eighth International Conference on Emerging Security Technologies (EST)
Thomas Truong Jonathan Graf Svetlana N Yanushkevich
222516633
Rank level Fusion Method That Improves Recognition Rate by Using Correlation Coefficient
Jung-Ho Ahn Jae Yeol Jeong Ik Rae Jeong
227864863
sanggwangyesureul iyonghayeo insigryuleul hyangsangsikin rank level fusion bangbeob
52928656
Score and Rank Level Fusion for Emotion Recognition Using Genetic Algorithm
Analysis of human body movement reveals information pertinent to efficient modeling of human behavior. Identification of the most contributing features, known to perform well in recognizing emotions, is a difficult task. This article proposes to use the binary chromosome based genetic algorithm to determine a subset of features that maximizes the accuracy of four expert systems developed for emotion recognition. The approach also identifies essential features for each of the expert models by selecting the minimum subset required to maximize the recognition accuracy. Later, the expert models are fused using score level and rank level fusion algorithms to further improve the performance of the system. Fusion of the expert models achieved an overall emotion recognition rate of 80% on a Kinect galt database.
2018 IEEE 17th International Conference on Cognitive Informatics Cognitive Computing (ICCI*CC)
Ferdous Ahmed Brandon Sieu Marina L Gavrilova
202766249
Rank Level Fusion Based on Multiple Iris Biometric Instances
In the pattern recognition field biometric system is considered as one of the important application. It is observed that unimodal biometric recognition systems tend to provide less accurateness, due to restricted degree of freedom, intra class variations, non universality, error rates which cannot be acceptable. Most of the researchers are focusing on multimodal biometric recognition system. These biometric systems fuses more than one biometric traits at different levels. But these systems very expensive due to the requirement of more than one sensor for image acquisition. This paper proposes, multi instance biometric recognition systems which combines both left and right iris at rank level fusion. The iris is rich in texture features and these are extracted using Local Binary Pattern (LBP) Gray Level Co occurrence Matrix (GLCM) Gabor techniques. Distance based classifier is used for classification. The classified output is given to Modified Borda count (MBC) to obtain the ranks. Results shows that the proposed technique outperforms than unimodal biometric recognition system.
Dr V Sireesha Sandhya Rani Kasi Reddy