Performance Analysis of Developed Multimodal Biometric Identity Verification System

The bank client identity verification system developed in the course of the IDENT project is presented. The total number of five biometric modalities including: dynamic handwritten signature proofing, voice recognition, face image verification, face contour extraction and hand blood vessels distribution comparison have been developed and studied. The experimental data were acquired employing multiple biometric sensors installed at engineered biometric terminals. The biometric portraits of more than 10 000 bank clients were registered and stored in the database during the presented study and then verified experimentally. Problem- specific survey was done on the basis of questionnaires completed by the subjects in order to assess the look and feel of the developed biometric system as well as to collect opinions concerning its implementation in banking outlets. A discussion concerning the quality of registered data and results achieved in the study is included.

[1]  M. Szczodrak,et al.  Face detection algorithms evaluation for the bank client verification , 2016, 2016 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA).

[2]  Fred Nicolls,et al.  Active shape models with SIFT descriptors and MARS , 2015, 2014 International Conference on Computer Vision Theory and Applications (VISAPP).

[3]  Andrzej Czyzewski,et al.  A handwritten signature verification method employing a tablet , 2016, 2016 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA).

[4]  Qingyang Hong,et al.  GMM-UBM for text-dependent speaker recognition , 2012, 2012 International Conference on Audio, Language and Image Processing.

[5]  Ratnadeep R. Deshmukh,et al.  Face recognition using fusion of PCA and LDA: Borda count approach , 2016, 2016 24th Mediterranean Conference on Control and Automation (MED).

[6]  Andrzej Czyzewski,et al.  Face Profile View Retrieval Using Time of Flight Camera Image Analysis , 2015, PReMI.

[7]  Matti Pietikäinen,et al.  Bi-Modal Person Recognition on a Mobile Phone: Using Mobile Phone Data , 2012, 2012 IEEE International Conference on Multimedia and Expo Workshops.

[8]  Vijay H. Mankar,et al.  Recognition of Faces Using Discriminative Features of LBP and HOG Descriptor in Varying Environment , 2015, 2015 International Conference on Computational Intelligence and Communication Networks (CICN).

[9]  Anil K. Jain,et al.  Open source biometric recognition , 2013, 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS).