List of Articles Biometrics

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  1 - Eye Gaze Detection Based on Learning Automata by Using SURF Descriptor
  Hassan Farsi Reza Nasiripour Sajad Mohammadzadeh
  10.7508/jist.2018.21.006
  In the last decade, eye gaze detection system is one of the most important areas in image processing and computer vision. The performance of eye gaze detection system depends on iris detection and recognition (IR). Iris recognition is very important role for person iden More

  In the last decade, eye gaze detection system is one of the most important areas in image processing and computer vision. The performance of eye gaze detection system depends on iris detection and recognition (IR). Iris recognition is very important role for person identification. The aim of this paper is to achieve higher recognition rate compared to learning automata based methods. Usually, iris retrieval based systems consist of several parts as follows: pre-processing, iris detection, normalization, feature extraction and classification which are captured from eye region. In this paper, a new method without normalization step is proposed. Meanwhile, Speeded up Robust Features (SURF) descriptor is used to extract features of iris images. The descriptor of each iris image creates a vector with 64 dimensions. For classification step, learning automata classifier is applied. The proposed method is tested on three known iris databases; UBIRIS, MMU and UPOL database. The proposed method results in recognition rate of 100% for UBIRIS and UPOL databases and 99.86% for MMU iris database. Also, EER rate of the proposed method for UBIRIS, UPOL and MMU iris database are 0.00%, 0.00% and 0.008%, respectively. Experimental results show that the proposed learning automata classifier results in minimum classification error, and improves precision and computation time. Manuscript profile
• Open Access Article
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  2 - A Dynamic Sequential Approach Using Deep Learning to Improve the Performance of Biometrics Match on Card Systems
  Mohammad Sabri Mohammad Moin Farbod Razzazi
  Nowadays, the threats such as terrorism and cybercrime are extremely increased, therefore, the identity authentication process is very substantial for the national security of a country. In this paper, we propose a novel multimodal authentication system with sequential More

  Nowadays, the threats such as terrorism and cybercrime are extremely increased, therefore, the identity authentication process is very substantial for the national security of a country. In this paper, we propose a novel multimodal authentication system with sequential structure based on deep learning. In the proposed method, feature vectors are extracted automatically through deep network with an end to end architecture. A multi biometric system using two fingerprint and a face is implemented and evaluated. The results demonstrate that, the authentication is done by fingerprints in 91.42% cases and only for 8.58% cases the face modal is required. In addition, the proposed method is more accurate than first and second fingerprint by 35% and 30% at FMR=0.001, respectively. As a result, we augmented the accuracy of the system and at the same time reduced the acquisition and matching time. This conducts to the improvement of user convenience and security of the service provider, simultaneously. The achievements of this work can be used to increase the effectiveness of authentication process and can play an important role in the acceptability of real world applications. Manuscript profile