Deep user identification model with multiple biometric data
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RESEARCH
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Deep user identification model with multiple biometric data Hyoung-Kyu Song1 , Ebrahim AlAlkeem2,3 , Jaewoong Yun4 , Tae-Ho Kim5 , Hyerin Yoo4 , Dasom Heo4 , Myungsu Chae4 and Chan Yeob Yeun2,3* 13th International Workshop on Data and Text Mining in Biomedical Informatics (DTMBIO 2019) *Correspondence: [email protected] 2 Electrical Engineering and Computer Science Department, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates 3 Center for Cyber-Physical Systems, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates Full list of author information is available at the end of the article
Abstract Background: Recognition is an essential function of human beings. Humans easily recognize a person using various inputs such as voice, face, or gesture. In this study, we mainly focus on DL model with multi-modality which has many benefits including noise reduction. We used ResNet-50 for extracting features from dataset with 2D data. Results: This study proposes a novel multimodal and multitask model, which can both identify human ID and classify the gender in single step. At the feature level, the extracted features are concatenated as the input for the identification module. Additionally, in our model design, we can change the number of modalities used in a single model. To demonstrate our model, we generate 58 virtual subjects with public ECG, face and fingerprint dataset. Through the test with noisy input, using multimodal is more robust and better than using single modality. Conclusions: This paper presents an end-to-end approach for multimodal and multitask learning. The proposed model shows robustness on the spoof attack, which can be significant for bio-authentication device. Through results in this study, we suggest a new perspective for human identification task, which performs better than in previous approaches. Keywords: Person identification, Multimodal learning, Multitask learning Background Recognition is an essential function of human beings. Humans easily recognize a person using various inputs such as voice, face, or gesture. Thus, when engaging with deep learning, multi-modality needs to be taken into account instead of single modality. Using multi-modality has many benefits including noise reduction. As any single modality can be easily contaminated due to powerline, EMG, channel noise, electrodes, and motion artifact, it is difficult to build a single modal recognition algorithm. For multimodal model, if a modality has a low signal to noise ratio, it can be compensated by another modality, so the overall system performance is maintained. Additionally, in the training phase, correlated features among modalities can be trained, and this may lead to
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