A Survey on Iris Recognition System

Kumar, Prabhat; Ahirwar, Manish Kumar; Deen, Anjna Jayant

doi:10.26438/ijcse/v7i7.302307

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Biometric recognition, which identifies individuals using biometric features, is an essential field of computer vision. Biometric recognition includes iris recognition [1], fingerprint recognition [2], face recognition [3] and gait recognition [4]. In law enforcement scenarios in which suspects are being arrested, it is difficult for the police and related institutions to obtain the fingerprints or facial features of strangers.…”

Section: Introductionmentioning

confidence: 99%

CJAM: Convolutional Neural Network Joint Attention Mechanism in Gait Recognition

Jia

Zhao

et al. 2021

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

Gait recognition distinguishes one individual from others according to the natural patterns of human gaits. Gait recognition is a challenging signal processing technology for biometric identification due to the ambiguity of contours and the complex feature extraction procedure. In this work, we proposed a new model -the convolutional neural network (CNN) joint attention mechanism (CJAM) -to classify the gait sequences and conduct person identification using the CASIA-A and CASIA-B gait datasets. The CNN model has the ability to extract gait features, and the attention mechanism continuously focuses on the most discriminative area to achieve person identification. We present a comprehensive transformation from gait image preprocessing to final identification. The results from 12 experiments show that the new attention model leads to a lower error rate than others. The CJAM model improved the 3D-CNN, CNN-LSTM (long short-term memory), and the simple CNN by 8.44%, 2.94% and 1.45%, respectively.

show abstract

Section: Introductionmentioning

confidence: 99%