Deep Feature Fusion for Iris and Periocular Biometrics on Mobile Devices

• Published in: IEEE transactions on information forensics and security Vol. 13; no. 11; pp. 2897 - 2912
• Main Authors: Zhang, Qi, Li, Haiqing, Sun, Zhenan, Tan, Tieniu
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: adaptive weights; Artificial neural networks; Biometric recognition systems; Biometrics; deep feature fusion; Electronic devices; Fuses; Identification methods; Image enhancement; Image quality; Iris recognition; Lighting; Machine learning; mobile devices; Mobile handsets; Object recognition; periocular recognition; Representations; Sensors
• ISSN: 1556-6013; 1556-6021
• DOI: 10.1109/TIFS.2018.2833033

The quality of iris images on mobile devices is significantly degraded due to hardware limitations and less constrained environments. Traditional iris recognition methods cannot achieve high identification rate using these low-quality images. To enhance the performance of mobile identification, we develop a deep feature fusion network that exploits the complementary information presented in iris and periocular regions. The proposed method first applies maxout units into the convolutional neural networks (CNNs) to generate a compact representation for each modality and then fuses the discriminative features of two modalities through a weighted concatenation. The parameters of convolutional filters and fusion weights are simultaneously learned to optimize the joint representation of iris and periocular biometrics. To promote the iris recognition research on mobile devices under near-infrared (NIR) illumination, we publicly release the CASIA-Iris-Mobile-V1.0 database, which in total includes 11 000 NIR iris images of both eyes from 630 Asians. It is the largest NIR mobile iris database as far as we know. On the newly built CASIA-Iris-M1-S3 data set, the proposed method achieves 0.60% equal error rate and 2.32% false non-match rate at false match rate =10 -5 , which are obviously better than unimodal biometrics as well as traditional fusion methods. Moreover, the proposed model requires much fewer storage spaces and computational resources than general CNNs.