Published on Tue Feb 16 2021

Selfie Periocular Verification using an Efficient Super-Resolution Approach

Juan Tapia, Marta Gomez-Barrero, Rodrigo Lara, Andres Valenzuela, Christoph Busch

Selfie-based biometrics has great potential for a wide range of applications. Most of the state of the art super-resolution methods use deep networks with large filters. Our method drastically reduces the number of parameters when compared with Deep CNNs.

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Abstract

Selfie-based biometrics has great potential for a wide range of applications from marketing to higher security environments like online banking. This is now especially relevant since e.g. periocular verification is contactless, and thereby safe to use in pandemics such as COVID-19. However, selfie-based biometrics faces some challenges since there is limited control over the data acquisition conditions. Therefore, super-resolution has to be used to increase the quality of the captured images. Most of the state of the art super-resolution methods use deep networks with large filters, thereby needing to train and store a correspondingly large number of parameters, and making their use difficult for mobile devices commonly used for selfie-based. In order to achieve an efficient super-resolution method, we propose an Efficient Single Image Super-Resolution (ESISR) algorithm, which takes into account a trade-off between the efficiency of the deep neural network and the size of its filters. To that end, the method implements a novel loss function based on the Sharpness metric. This metric turns out to be more suitable for increasing the quality of the eye images. Our method drastically reduces the number of parameters when compared with Deep CNNs with Skip Connection and Network (DCSCN): from 2,170,142 to 28,654 parameters when the image size is increased by a factor of x3. Furthermore, the proposed method keeps the sharp quality of the images, which is highly relevant for biometric recognition purposes. The results on remote verification systems with raw images reached an Equal Error Rate (EER) of 8.7% for FaceNet and 10.05% for VGGFace. Where embedding vectors were used from periocular images the best results reached an EER of 8.9% (x3) for FaceNet and 9.90% (x4) for VGGFace.

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Computer Vision
Iris Liveness Detection using a Cascade of Dedicated Deep Learning Networks
Presentation attacks can bypass biometric authentication systems using artefacts such as printed images, artificial eyes, textured contact lenses, etc. Many liveness detection methods have been proposed to improve the security of these systems. This paper proposes a serial architecture based on a MobileNetV2 modification trained to classify bona fide iris images versus presentation attack images.
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Sat Feb 15 2020
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SIP-SegNet: A Deep Convolutional Encoder-Decoder Network for Joint Semantic Segmentation and Extraction of Sclera, Iris and Pupil based on Periocular Region Suppression
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Tue Jan 19 2021
Artificial Intelligence
Analysis and evaluation of Deep Learning based Super-Resolution algorithms to improve performance in Low-Resolution Face Recognition
Super-resolution approaches were proposed to enhance image quality for human-level perception. Biometrics super-resolution methods seek the best computer perception version of the image. This project aimed at evaluating and adapting different neural network architectures for the task of face super- resolution driven by face recognition performance in low-resolution images.
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Wed Aug 29 2018
Computer Vision
The Impact of Preprocessing on Deep Representations for Iris Recognition on Unconstrained Environments
The use of iris as a biometric trait is widely used because of its high level of distinction and uniqueness. The acquired iris are affected by capture distance, rotation, blur, motion blur, low contrast and specular reflection, creating noises that disturb the iris recognition systems.
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Wed Feb 12 2020
Computer Vision
CNN Hyperparameter tuning applied to Iris Liveness Detection
The iris pattern has significantly improved the biometric recognition field. Spoofing techniques can be used to bypass biometric systems. The first Internacional Iris Liveness Detection competition was launched in 2013.
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