The impact of collarette region-based convolutional neural network for iris recognition


  • Souheila Tounsi Higher School of Industrial Technologies (ESTI)
  • Karima Boukari Badji-Mokhtar University
  • Abdourazek Souahi Badji-Mokhtar University, Faculty of Sciences, Department of mathematics, Laboratory of advanced materials, Badji Mokhtar-Annaba University, Annaba, Algeria Annaba University, P.O. Box 12, 23000 Annaba, Algeria



Iris Recognition, Collarette zigzag, CNN, CASIA-Iris-Lamp V4, biometric, Automatic speech recognition, SVM, MLP, ANN, MFCC, FPGA.


Iris recognition is a biometric technique that reliably and quickly recognizes a person by their iris based on unique biological characteristics. Iris has an exceptional structure and it provides very rich feature spaces as freckles, stripes, coronas, zigzag collarette area, etc. It has many features where its growing interest in biometric recognition lies. This paper proposes an improved iris recognition method for person identification based on Convolutional Neural Networks (CNN) with an improved recognition rate based on a contribution on zigzag collarette area - the area surrounding the pupil - recognition. Our work is in the field of biometrics especially iris recognition; the iris recognition rate using the full circle of the zigzag collarette was compared with the detection rate using the lower semicircle of the zigzag collarette. The classification of the collarette is based on the Alex-Net model to learn this feature, the use of the couple (collarette/CNN) allows for noiseless and more targeted characterization and also an automatic extraction of the lower semicircle of the collarette region, finally, the SVM training model is used for classification using grayscale eye image data taken from (CASIA-iris-V4) database. The experimental results show that our contribution proves to be the best accurate, because the CNN can effectively extract the image features with higher classification accuracy and because our new method, which uses the lower semicircle of the collarette region, achieved the highest recognition accuracy compared with the oldĀ  methods that use
the full circle of collarette region.

Author Biography

Karima Boukari, Badji-Mokhtar University

Faculty of Engineering, Department of electronics,

Laboratory for Study and Research in Instrumentation and Communication Annaba (LERICA),






Original Scientific Papers