Deep user identification model with multiple biometric data

BMC Bioinformatics

Ebrahim AlAlkeem¹  Chan Yeob Yeun¹  Tae-Ho Kim²  Hyoung-Kyu Song³  Hyerin Yoo⁴  Dasom Heo⁴  Jaewoong Yun⁴  Myungsu Chae⁴ 

[1] Electrical Engineering and Computer Science Department, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates;Center for Cyber-Physical Systems, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates;Institute for Artificial Intelligence, Korea Advanced Institute of Science and Technology, Daejeon, South Korea;Korea Advanced Institute of Science and Technology, Daejeon, South Korea;Research Institute, NOTA Incorporated, Gangnam-gu, Seoul, South Korea;

关键词: Person identification;    Multimodal learning;    Multitask learning;   

DOI  :  10.1186/s12859-020-03613-3

来源: Springer

【 摘 要 】

BackgroundRecognition 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.ResultsThis 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.ConclusionsThis 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.

【 授权许可】

CC BY